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: "human re identification by matching compositional template with cluster sampling"
  Abstract: This paper aims at a newly raising task in visual surveillance: re-identifying people at a distance by matching body information, given several reference examples. Most of existing works solve this task by matching a reference template with the target individual, but often suffer from large human appearance variability (e.g. different poses/views, illumination) and high false positives in matching caused by conjunctions, occlusions or surrounding clutters. Addressing these problems, we construct a simple yet expressive template from a few reference images of a certain individual, which represents the body as an articulated assembly of compositional and alternative parts, and propose an effective matching algorithm with cluster sampling. This algorithm is designed within a candidacy graph whose vertices are matching candidates (i.e. a pair of source and target body parts), and iterates in two steps for convergence. (i) It generates possible partial matches based on compatible and competitive relations among body parts. (ii) It confirms the partial matches to generate a new matching solution, which is accepted by the Markov Chain Monte Carlo (MCMC) mechanism. In the experiments, we demonstrate the superior performance of our approach on three public databases compared to existing methods.

Connected through hyperedge HE-1 (induced by source paper "few shot deep adversarial learning for video based person re identification"):
  with members:
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "crossing generative adversarial networks for cross view person re identification"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "person re identification via recurrent feature aggregation"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"

Connected through hyperedge HE-2 (induced by source paper "denserac joint 3d pose and shape estimation by dense render and compare"):
  with members:
    - "shape from mask a deep learning based human body shape reconstruction from binary mask images"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "bodynet volumetric inference of 3d human body shapes"
    - "densereg fully convolutional dense shape regression in the wild"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "3d human pose estimation in the wild by adversarial learning"

Connected through hyperedge HE-3 (induced by source paper "part aligned bilinear representations for person re identification"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "person re identification by camera correlation aware feature augmentation"
    - "video based person re identification with accumulative motion context"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "scalable person re identification on supervised smoothed manifold"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "deep mutual learning"

Connected through hyperedge HE-4 (induced by source paper "densely semantically aligned person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "improving person re identification by attribute and identity learning"
    - "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"
    - "densereg fully convolutional dense shape regression in the wild"
    - "attention aware compositional network for person re identification"
    - "dual attention matching network for context aware feature sequence based person re identification"

Connected through hyperedge HE-5 (induced by source paper "what and where to match deep spatially multiplicative integration networks for person re identification"):
  with members:
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "person re identification by camera correlation aware feature augmentation"
    - "scalable person re identification on supervised smoothed manifold"
    - "end to end comparative attention networks for person re identification"
    - "diversified visual attention networks for fine grained object classification"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "embedding deep metric for person re identication a study against large variations"
    - "learning a discriminative null space for person re identification"

Connected through hyperedge HE-6 (induced by source paper "learning correspondence structures for person re identification"):
  with members:
    - "learning a discriminative null space for person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "deep ranking for person re identification via joint representation learning"
    - "an enhanced deep feature representation for person re identification"
    - "deep feature learning with relative distance comparison for person re identification"
    - "learning descriptors for object recognition and 3d pose estimation"
    - "person re identification with correspondence structure learning"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"

Connected through hyperedge HE-7 (induced by source paper "person re identification via recurrent feature aggregation"):
  with members:
    - "recurrent attention models for depth based person identification"
    - "person re identification by discriminative selection in video ranking"
    - "person re identification meets image search"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "caffe convolutional architecture for fast feature embedding"
    - "positive semidefinite metric learning using boosting like algorithms"

Connected through hyperedge HE-8 (induced by source paper "learning compact appearance representation for video based person re identification"):
  with members:
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "deep ranking for person re identification via joint representation learning"
    - "top push video based person re identification"
    - "deep feature learning with relative distance comparison for person re identification"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "return of the devil in the details delving deep into convolutional nets"

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)