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: "deeply learned part aligned representations for person re identification"
  Abstract: In this paper, we address the problem of person re-identification, which refers to associating the persons captured from different cameras. We propose a simple yet effective human part-aligned representation for handling the body part misalignment problem. Our approach decomposes the human body into regions (parts) which are discriminative for person matching, accordingly computes the representations over the regions, and aggregates the similarities computed between the corresponding regions of a pair of probe and gallery images as the overall matching score. Our formulation, inspired by attention models, is a deep neural network modeling the three steps together, which is learnt through minimizing the triplet loss function without requiring body part labeling information. Unlike most existing deep learning algorithms that learn a global or spatial partition-based local representation, our approach performs human body partition, and thus is more robust to pose changes and various human spatial distributions in the person bounding box. Our approach shows state-of-the-art results over standard datasets, Market-$1501$, CUHK$03$, CUHK$01$ and VIPeR.

Connected through hyperedge HE-1 (induced by source paper "person re identification with deep similarity guided graph neural network"):
  with members:
    - "end to end deep kronecker product matching for person re identification"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "harmonious attention network for person re identification"
    - "multi channel pyramid person matching network for person re identification"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "deep group shuffling random walk for person re identification"
    - "vip cnn visual phrase guided convolutional neural network"
    - "random erasing data augmentation"

Connected through hyperedge HE-2 (induced by source paper "feature affinity based pseudo labeling for semi supervised person re identification"):
  with members:
    - "multi pseudo regularized label for generated data in person re identification"
    - "feature mask network for person re identification"
    - "improving person re identification by attribute and identity learning"
    - "weighted bilinear coding over salient body parts for person re identification"
    - "scalable person re identification on supervised smoothed manifold"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "re ranking person re identification with k reciprocal encoding"
    - "person re identification by deep joint learning of multi loss classification"

Connected through hyperedge HE-3 (induced by source paper "support neighbor loss for person re identification"):
  with members:
    - "in defense of the triplet loss for person re identification"
    - "scalable person re identification on supervised smoothed manifold"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "deep mutual learning"
    - "camera style adaptation for person re identification"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "re ranking person re identification with k reciprocal encoding"
    - "person re identification by deep joint learning of multi loss classification"

Connected through hyperedge HE-4 (induced by source paper "in defense of the classification loss for person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "deep mutual learning"
    - "glad global local alignment descriptor for pedestrian retrieval"
    - "person re identification by deep joint learning of multi loss classification"
    - "alignedreid surpassing human level performance in person re identification"
    - "hydraplus net attentive deep features for pedestrian analysis"

Connected through hyperedge HE-5 (induced by source paper "end to end deep kronecker product matching for person re identification"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "3d facial expression reconstruction using cascaded regression"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "person re identification by deep joint learning of multi loss classification"
    - "hydraplus net attentive deep features for pedestrian analysis"

Connected through hyperedge HE-6 (induced by source paper "deep group shuffling random walk for person re identification"):
  with members:
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "re ranking person re identification with k reciprocal encoding"
    - "person re identification by deep joint learning of multi loss classification"
    - "cross view asymmetric metric learning for unsupervised person re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"

Connected through hyperedge HE-7 (induced by source paper "fd gan pose guided feature distilling gan for robust person re identification"):
  with members:
    - "deformable gans for pose based human image generation"
    - "perceptual generative adversarial networks for small object detection"
    - "learning deep context aware features over body and latent parts for person re identification"
    - "svdnet for pedestrian retrieval"
    - "learning a discriminative null space for person re identification"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "stacked generative adversarial networks"
    - "learning deep feature representations with domain guided dropout for person re identification"

Connected through hyperedge HE-8 (induced by source paper "deep saliency hashing"):
  with members:
    - "deep triplet quantization"
    - "pose invariant embedding for deep person re identification"
    - "diversified visual attention networks for fine grained object classification"
    - "fully convolutional attention localization networks efficient attention localization for fine grained recognition"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "faster r cnn towards real time object detection with region proposal networks"

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)