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: "maximum margin metric learning over discriminative nullspace for person re identification"
  Abstract: In this paper we propose a novel metric learning framework called Nullspace Kernel Maximum Margin Metric Learning (NK3ML) which efficiently addresses the small sample size (SSS) problem inherent in person re-identification and offers a significant performance gain over existing state-of-the-art methods. Taking advantage of the very high dimensionality of the feature space, the metric is learned using a maximum margin criterion (MMC) over a discriminative nullspace where all training sample points of a given class map onto a single point, minimizing the within class scatter. A kernel version of MMC is used to obtain a better between class separation. Extensive experiments on four challenging benchmark datasets for person re-identification demonstrate that the proposed algorithm outperforms all existing methods. We obtain 99.8% rank-1 accuracy on the most widely accepted and challenging dataset VIPeR, compared to the previous state of the art being only 63.92%.

Connected through hyperedge HE-1 (induced by source paper "knowledge distillation for end to end person search"):
  with members:
    - "learning context graph for person search"
    - "query guided end to end person search"
    - "person search by multi scale matching"
    - "model compression via distillation and quantization"
    - "attention aware compositional network for person re identification"
    - "person re identification with deep similarity guided graph neural network"
    - "person search via a mask guided two stream cnn model"
    - "knowledge distillation in generations more tolerant teachers educate better students"

Connected through hyperedge HE-2 (induced by source paper "cross view kernel similarity metric learning using pairwise constraints for person re identification"):
  with members:
    - "multiple kernel fisher discriminant metric learning for person re identification"
    - "person re identification with metric learning using privileged information"
    - "a semi supervised maximum margin metric learning approach for small scale person re identification"
    - "graph correspondence transfer for person re identification"
    - "person re identification in identity regression space"
    - "scalable person re identification on supervised smoothed manifold"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "re ranking person re identification with k reciprocal encoding"

Connected through hyperedge HE-3 (induced by source paper "optimizing speed accuracy trade off for person re identification via knowledge distillation"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "learning incremental triplet margin for person re identification"
    - "multi task mid level feature alignment network for unsupervised cross dataset person re identification"
    - "re identification with consistent attentive siamese networks"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "pyramidal person re identification via multi loss dynamic training"
    - "fd gan pose guided feature distilling gan for robust person re identification"
    - "deep mutual learning"

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)