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: "scpnet spatial channel parallelism network for joint holistic and partial person re identification"
  Abstract: Holistic person re-identification (ReID) has received extensive study in the past few years and achieves impressive progress. However, persons are often occluded by obstacles or other persons in practical scenarios, which makes partial person re-identification non-trivial. In this paper, we propose a spatial-channel parallelism network (SCPNet) in which each channel in the ReID feature pays attention to a given spatial part of the body. The spatial-channel corresponding relationship supervises the network to learn discriminative feature for both holistic and partial person re-identification. The single model trained on four holistic ReID datasets achieves competitive accuracy on these four datasets, as well as outperforms the state-of-the-art methods on two partial ReID datasets without training.

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"
    - "maskreid a mask based deep ranking neural network for 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"
    - "maskreid a mask based deep ranking neural network for person re identification"
    - "horizontal pyramid matching for person re identification"

Connected through hyperedge HE-3 (induced by source paper "diversity achieving slow dropblock network for person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for 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"
    - "dropblock a regularization method for convolutional networks"
    - "attention aware compositional network for person re identification"

Connected through hyperedge HE-4 (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"
    - "maskreid a mask based deep ranking neural network for person re identification"
    - "features for multi target multi camera tracking and re identification"

Connected through hyperedge HE-5 (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-6 (induced by source paper "bag of tricks and a strong baseline for deep person re identification"):
  with members:
    - "batch dropblock network for person re identification and beyond"
    - "harmonious attention network for person re identification"
    - "maskreid a mask based deep ranking neural network for person re identification"
    - "features for multi target multi camera tracking and re identification"
    - "a coarse to fine pyramidal model for person re identification via multi loss dynamic training"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "pyramidal person re identification via multi loss dynamic training"
    - "human semantic parsing for person re identification"

Connected through hyperedge HE-7 (induced by source paper "ranking and classification driven feature learning for person re_identification"):
  with members:
    - "deep constrained dominant sets for person re identification"
    - "ranked list loss for deep metric learning"
    - "omni scale feature learning for person re identification"
    - "bag of tricks and a strong baseline for deep 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"
    - "pyramidal person re identification via multi loss dynamic training"

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)