human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "spatial temporal synergic residual learning for video person re identification"):
  Abstract: We tackle the problem of person re-identification in video setting in this paper, which has been viewed as a crucial task in many applications. Meanwhile, it is very challenging since the task requires learning effective representations from video sequences with heterogeneous spatial-temporal information. We present a novel method - Spatial-Temporal Synergic Residual Network (STSRN) for this problem. STSRN contains a spatial residual extractor, a temporal residual processor and a spatial-temporal smooth module. The smoother can alleviate sample noises along the spatial-temporal dimensions thus enable STSRN extracts more robust spatial-temporal features of consecutive frames. Extensive experiments are conducted on several challenging datasets including iLIDS-VID, PRID2011 and MARS. The results demonstrate that the proposed method achieves consistently superior performance over most of state-of-the-art methods.

Includes members:
  Member "video person re identification by temporal residual learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cdpm convolutional deformable part models for semantically aligned person re identification")
  Member "quality aware network for set to set recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "derivative manipulation for general example weighting")
  Member "jointly attentive spatial temporal pooling networks for video based person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "person re identification by contour sketch under moderate clothing change")
  Member "three stream convolutional networks for video based person re identification":
    also connected through other hyperedges:
      (none)
  Member "fully adaptive feature sharing in multi task networks with applications in person attribute classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mti net multi scale task interaction networks for multi task learning")
  Member "walk and learn facial attribute representation learning from egocentric video and contextual data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "branched multi task networks deciding what layers to share")
  Member "person re identification past present and future":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")
  Member "deep recurrent convolutional networks for video based person re identification an end to end approach":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scan self and collaborative attention network for video person re identification")

Question: Please tell me which class the source paper 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)