human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "person re identification with metric learning using privileged information"
  Abstract: Despite the promising progress made in recent years, person re-identification remains a challenging task due to complex variations in human appearances from different camera views. This paper presents a logistic discriminant metric learning method for this challenging problem. Different with most existing metric learning algorithms, it exploits both original data and auxiliary data during training, which is motivated by the new machine learning paradigm - Learning Using Privileged Information. Such privileged information is a kind of auxiliary knowledge which is only available during training. Our goal is to learn an optimal distance function by constructing a locally adaptive decision rule with the help of privileged information. We jointly learn two distance metrics by minimizing the empirical loss penalizing the difference between the distance in the original space and that in the privileged space. In our setting, the distance in the privileged space functions as a local decision threshold, which guides the decision making in the original space like a teacher. The metric learned from the original space is used to compute the distance between a probe image and a gallery image during testing. In addition, we extend the proposed approach to a multi-view setting which is able to explore the complementation of multiple feature representations. In the multi-view setting, multiple metrics corresponding to different original features are jointly learned, guided by the same privileged information. Besides, an effective iterative optimization scheme is introduced to simultaneously optimize the metrics and the assigned metric weights. Experiment results on several widely-used datasets demonstrate that the proposed approach is superior to global decision threshold based methods and outperforms most state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "cross spectrum dual subspace pairing for rgb infrared cross modality person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "feature affinity based pseudo labeling for semi supervised person re identification"
    - "person re identification with deep similarity guided graph neural network"
    - "fd gan pose guided feature distilling gan for robust person re identification"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "in defense of the triplet loss for person re identification"
    - "deep mutual learning"
    - "beyond triplet loss a deep quadruplet network for person re identification"

Connected through hyperedge HE-2 (induced by source paper "cdpm convolutional deformable part models for semantically aligned person re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep representation learning with part loss for person re identification"
    - "video person re identification by temporal residual learning"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "learning view specific deep networks for person re identification"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"

Connected through hyperedge HE-3 (induced by source paper "an end to end foreground aware network for person re identification"):
  with members:
    - "interaction and aggregation network for person re identification"
    - "perceive where to focus learning visibility aware part level features for partial person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "learning incremental triplet margin for person re identification"
    - "harmonious attention network for person re identification"
    - "attention aware compositional network for person re identification"
    - "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns"

Connected through hyperedge HE-4 (induced by source paper "towards better validity dispersion based clustering for unsupervised person re identification"):
  with members:
    - "learning view specific deep networks for person re identification"
    - "feature affinity based pseudo labeling for semi supervised person re identification"
    - "deep clustering for unsupervised learning of visual features"
    - "pamm pose aware multi shot matching for improving person re identification"
    - "deep association learning for unsupervised video person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "part aligned bilinear representations for person re identification"
    - "image image domain adaptation with preserved self similarity and domain dissimilarity for person re identification"

Connected through hyperedge HE-5 (induced by source paper "user diverse preference modeling by multimodal attentive metric learning"):
  with members:
    - "mmalfm explainable recommendation by leveraging reviews and images"
    - "metric factorization recommendation beyond matrix factorization"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "collaborative memory network for recommendation systems"
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "neural compatibility modeling with attentive knowledge distillation"
    - "neural collaborative filtering"
    - "joint deep modeling of users and items using reviews for recommendation"

Connected through hyperedge HE-6 (induced by source paper "cross view kernel similarity metric learning using pairwise constraints for person re identification"):
  with members:
    - "multiple kernel fisher discriminant metric learning for person re identification"
    - "a semi supervised maximum margin metric learning approach for small scale person re identification"
    - "maximum margin metric learning over discriminative nullspace for person re identification"
    - "graph correspondence transfer for person re identification"
    - "person re identification in identity regression space"
    - "scalable person re identification on supervised smoothed manifold"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "re ranking person re identification with k reciprocal encoding"

Connected through hyperedge HE-7 (induced by source paper "who where and what to wear extracting fashion knowledge from social media"):
  with members:
    - "low resource name tagging learned with weakly labeled data"
    - "kgat knowledge graph attention network for recommendation"
    - "yolov3 an incremental improvement"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "joint optimization framework for learning with noisy labels"
    - "aesthetic based clothing recommendation"
    - "transnfcm translation based neural fashion compatibility modeling"
    - "neural compatibility modeling with attentive knowledge distillation"

Connected through hyperedge HE-8 (induced by source paper "purifying real images with an attention guided style transfer network for gaze estimation"):
  with members:
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "cross entropy adversarial view adaptation for person re identification"
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "guiding intelligent surveillance system by learning by synthesis gaze estimation"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.CV(Computer Vision and Pattern Recognition)