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: "maskreid a mask based deep ranking neural network for person re identification"
  Abstract: Person retrieval faces many challenges including cluttered background, appearance variations (e.g., illumination, pose, occlusion) among different camera views and the similarity among different person's images. To address these issues, we put forward a novel mask based deep ranking neural network with a skipped fusing layer. Firstly, to alleviate the problem of cluttered background, masked images with only the foreground regions are incorporated as input in the proposed neural network. Secondly, to reduce the impact of the appearance variations, the multi-layer fusion scheme is developed to obtain more discriminative fine-grained information. Lastly, considering person retrieval is a special image retrieval task, we propose a novel ranking loss to optimize the whole network. The proposed ranking loss can further mitigate the interference problem of similar negative samples when producing ranking results. The extensive experiments validate the superiority of the proposed method compared with the state-of-the-art methods on many benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "disassembling the dataset a camera alignment mechanism for multiple tasks in person re identification"):
  with members:
    - "view confusion feature learning for person re identification"
    - "resolution invariant person re identification"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "eanet enhancing alignment for cross domain person re identification"
    - "multi level factorisation net for person re identification"
    - "harmonious attention network for person re identification"
    - "multi task mid level feature alignment network for unsupervised cross dataset person re identification"

Connected through hyperedge HE-2 (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"
    - "scpnet spatial channel parallelism network for joint holistic and partial person re identification"
    - "horizontal pyramid matching for person re identification"

Connected through hyperedge HE-3 (induced by source paper "progressive cross camera soft label learning for semi supervised person re identification"):
  with members:
    - "camera adversarial transfer for unsupervised person re identification"
    - "imitating targets from all sides an unsupervised transfer learning method for person re identification"
    - "unsupervised tracklet person re identification"
    - "a novel unsupervised camera aware domain adaptation framework for person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "label propagation for deep semi supervised learning"
    - "attribute aware attention model for fine grained representation learning"

Connected through hyperedge HE-4 (induced by source paper "semantics aligned representation learning for person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "re identification supervised texture generation"
    - "densebody directly regressing dense 3d human pose and shape from a single color image"
    - "improving person re identification by attribute and identity learning"
    - "learning discriminative features with multiple granularities for person re identification"
    - "densereg fully convolutional dense shape regression in the wild"
    - "attention aware compositional network for person re identification"
    - "horizontal pyramid matching for person re identification"

Connected through hyperedge HE-5 (induced by source paper "a strong baseline and batch normalization neck for deep person re identification"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "spherereid deep hypersphere manifold embedding for person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "batch dropblock network for person re identification and beyond"
    - "harmonious attention network for person re identification"
    - "features for multi target multi camera tracking and re identification"
    - "attention aware compositional network for person re identification"

Connected through hyperedge HE-6 (induced by source paper "attention network robustification for person reid"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "omni scale feature learning for person re identification"
    - "relation aware global attention"
    - "spherereid deep hypersphere manifold embedding for person re identification"
    - "auto reid searching for a part aware convnet for person re identification"
    - "abd net attentive but diverse person re identification"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "bag of tricks and a strong baseline for deep person re identification"

Connected through hyperedge HE-7 (induced by source paper "relation aware global attention"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "residual non local attention networks for image restoration"
    - "learning discriminative features with multiple granularities for person re identification"
    - "ocnet object context network for scene parsing"
    - "horizontal pyramid matching for person re identification"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "recognizing partial biometric patterns"
    - "survey on deep learning techniques for person re identification task"

Connected through hyperedge HE-8 (induced by source paper "greyreid a two stream deep framework with rgb grey information for person re identification"):
  with members:
    - "unsupervised person image generation with semantic parsing transformation"
    - "aanet attribute attention network for person re identifications"
    - "a novel unsupervised camera aware domain adaptation framework for person re identification"
    - "attribute aware attention model for fine grained representation learning"
    - "ca3net contextual attentional attribute appearance network for person re identification"
    - "re identification with consistent attentive siamese networks"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "human semantic parsing 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)