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: "deep attributes driven multi camera person re identification"
  Abstract: The visual appearance of a person is easily affected by many factors like pose variations, viewpoint changes and camera parameter differences. This makes person Re-Identification (ReID) among multiple cameras a very challenging task. This work is motivated to learn mid-level human attributes which are robust to such visual appearance variations. And we propose a semi-supervised attribute learning framework which progressively boosts the accuracy of attributes only using a limited number of labeled data. Specifically, this framework involves a three-stage training. A deep Convolutional Neural Network (dCNN) is first trained on an independent dataset labeled with attributes. Then it is fine-tuned on another dataset only labeled with person IDs using our defined triplet loss. Finally, the updated dCNN predicts attribute labels for the target dataset, which is combined with the independent dataset for the final round of fine-tuning. The predicted attributes, namely deep attributes exhibit superior generalization ability across different datasets. By directly using the deep attributes with simple Cosine distance, we have obtained surprisingly good accuracy on four person ReID datasets. Experiments also show that a simple distance metric learning modular further boosts our method, making it significantly outperform many recent works.

Connected through hyperedge HE-1 (induced by source paper "unsupervised person re identification by deep asymmetric metric embedding"):
  with members:
    - "person re identification by camera correlation aware feature augmentation"
    - "began boundary equilibrium generative adversarial networks"
    - "cross view asymmetric metric learning for unsupervised person re identification"
    - "end to end comparative attention networks for person re identification"
    - "transferring a semantic representation for person re identification and search"
    - "unsupervised person re identification clustering and fine tuning"
    - "person transfer gan to bridge domain gap for person re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"

Connected through hyperedge HE-2 (induced by source paper "interactive natural language based person search"):
  with members:
    - "an empirical study of person re identification with attributes"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "identity aware textual visual matching with latent co attention"
    - "co attending free form regions and detections with multi modal multiplicative feature embedding for visual question answering"
    - "person search with natural language description"
    - "adversarial attribute image person re identification"
    - "mask r cnn"
    - "vqa visual question answering"

Connected through hyperedge HE-3 (induced by source paper "regression based music emotion prediction using triplet neural networks"):
  with members:
    - "multimodal deep models for predicting affective responses evoked by movies"
    - "latent space representation for multi target speaker detection and identification with a sparse dataset using triplet neural networks"
    - "deep feature learning with relative distance comparison for person re identification"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-4 (induced by source paper "adaptive exploration for unsupervised person re identification"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "auto reid searching for a part aware convnet for person re identification"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "active multi kernel domain adaptation for hyperspectral image classification"
    - "self supervised video hashing with hierarchical binary auto encoder"
    - "dissecting person re identification from the viewpoint of viewpoint"
    - "multi task mid level feature alignment network for unsupervised cross dataset person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"

Connected through hyperedge HE-5 (induced by source paper "joint discriminative and generative learning for person re identification"):
  with members:
    - "multi pseudo regularized label for generated data in person re identification"
    - "cityflow a city scale benchmark for multi target multi camera vehicle tracking and re identification"
    - "improving person re identification by attribute and identity learning"
    - "multi level factorisation net for person re identification"
    - "adversarial open world person re identification"
    - "harmonious attention network for person re identification"
    - "multimodal unsupervised image to image translation"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"

Connected through hyperedge HE-6 (induced by source paper "improving person re identification by attribute and identity learning"):
  with members:
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "attention aware compositional network for person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "deep spatial feature reconstruction for partial person re identification alignment free approach"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "person search with natural language description"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "adversarial attribute image person re identification"

Connected through hyperedge HE-7 (induced by source paper "unified multifaceted feature learning for person re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "interaction and aggregation network for person re identification"
    - "a dual path model with adaptive attention for vehicle re identification"
    - "joint discriminative and generative learning for person re identification"
    - "deep representation learning with part loss for person re identification"
    - "abd net attentive but diverse person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "multi level factorisation net for person re identification"

Connected through hyperedge HE-8 (induced by source paper "beyond human parts dual part aligned representations for person re identification"):
  with members:
    - "interlaced sparse self attention for semantic segmentation"
    - "aanet attribute attention network for person re identifications"
    - "object contextual representations for semantic segmentation"
    - "attribute aware pooling for pedestrian attribute recognition"
    - "improving person re identification by attribute and identity learning"
    - "attribute aware attention model for fine grained representation learning"
    - "devil in the details towards accurate single and multiple human parsing"
    - "multi level factorisation net for person re identification"

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)