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: "occluded person re identification"
  Abstract: Person re-identification (re-id) suffers from a serious occlusion problem when applied to crowded public places. In this paper, we propose to retrieve a full-body person image by using a person image with occlusions. This differs significantly from the conventional person re-id problem where it is assumed that person images are detected without any occlusion. We thus call this new problem the occluded person re-identitification. To address this new problem, we propose a novel Attention Framework of Person Body (AFPB) based on deep learning, consisting of 1) an Occlusion Simulator (OS) which automatically generates artificial occlusions for full-body person images, and 2) multi-task losses that force the neural network not only to discriminate a person's identity but also to determine whether a sample is from the occluded data distribution or the full-body data distribution. Experiments on a new occluded person re-id dataset and three existing benchmarks modified to include full-body person images and occluded person images show the superiority of the proposed method.

Connected through hyperedge HE-1 (induced by source paper "high order information matters learning relation and topology for occluded person re identification"):
  with members:
    - "learning combinatorial embedding networks for deep graph matching"
    - "deep high resolution representation learning for human pose estimation"
    - "a novel teacher student learning framework for occluded person re identification"
    - "stnreid deep convolutional networks with pairwise spatial transformer networks for partial person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "maskreid a mask based deep ranking neural network for person re identification"
    - "scpnet spatial channel parallelism network for joint holistic and partial person re identification"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised person re id differentiable graphical learning and a new benchmark"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "aanet attribute attention network for person re identifications"
    - "joint discriminative and generative learning for person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "adaptively connected neural networks"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "discovering underlying person structure pattern with relative local distance for person re identification"
    - "perceive where to focus learning visibility aware part level features for partial person re identification"

Connected through hyperedge HE-3 (induced by source paper "adaptively connected neural networks"):
  with members:
    - "discovering underlying person structure pattern with relative local distance for person re identification"
    - "batch kalman normalization towards training deep neural networks with micro batches"
    - "spatial temporal person re identification"
    - "m2m gan many to many generative adversarial transfer learning for person re identification"
    - "large scale learnable graph convolutional networks"
    - "deformable convnets v2 more deformable better results"
    - "graph based global reasoning networks"
    - "random erasing data augmentation"

Connected through hyperedge HE-4 (induced by source paper "can adversarial networks hallucinate occluded people with a plausible aspect"):
  with members:
    - "learning to detect and track visible and occluded body joints in a virtual world"
    - "densepose dense human pose estimation in the wild"
    - "perceptual adversarial networks for image to image transformation"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "a generative model of people in clothing"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "multi view image generation from a single view"
    - "hydraplus net attentive deep features for pedestrian analysis"

Connected through hyperedge HE-5 (induced by source paper "perceive where to focus learning visibility aware part level features for partial person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "recognizing partial biometric patterns"
    - "human semantic parsing for person re identification"
    - "deep spatial feature reconstruction for partial person re identification alignment free approach"
    - "transitive invariance for self supervised visual representation learning"
    - "in defense of the triplet loss for person re identification"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "glad global local alignment descriptor for pedestrian retrieval"

Connected through hyperedge HE-6 (induced by source paper "a novel teacher student learning framework for occluded person re identification"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "multi level factorisation net for person re identification"
    - "distractor aware siamese networks for visual object tracking"
    - "human semantic parsing for person re identification"
    - "deep spatial feature reconstruction for partial person re identification alignment free approach"
    - "the devil is in the middle exploiting mid level representations for cross domain instance matching"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"

Connected through hyperedge HE-7 (induced by source paper "foreground aware pyramid reconstruction for alignment free occluded person re identification"):
  with members:
    - "devil in the details towards accurate single and multiple human parsing"
    - "learning discriminative features with multiple granularities for person re identification"
    - "harmonious attention network for person re identification"
    - "maskreid a mask based deep ranking neural network for person re identification"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "human semantic parsing for person re identification"
    - "part aligned bilinear representations for person re identification"
    - "deep spatial feature reconstruction for partial person re identification alignment free approach"

Connected through hyperedge HE-8 (induced by source paper "spatial temporal person re identification"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns"
    - "m2m gan many to many generative adversarial transfer learning for person re identification"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "cross domain visual matching via generalized similarity measure and feature learning"
    - "learning deep context aware features over body and latent parts for person re identification"
    - "joint person re identification and camera network topology inference in multiple cameras"

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)