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 "relevance subject machine a novel person re identification framework"):
  Abstract: We propose a novel method called the Relevance Subject Machine (RSM) to solve the person re-identification (re-id) problem. RSM falls under the category of Bayesian sparse recovery algorithms and uses the sparse representation of the input video under a pre-defined dictionary to identify the subject in the video. Our approach focuses on the multi-shot re-id problem, which is the prevalent problem in many video analytics applications. RSM captures the essence of the multi-shot re-id problem by constraining the support of the sparse codes for each input video frame to be the same. Our proposed approach is also robust enough to deal with time varying outliers and occlusions by introducing a sparse, non-stationary noise term in the model error. We provide a novel Variational Bayesian based inference procedure along with an intuitive interpretation of the proposed update rules. We evaluate our approach over several commonly used re-id datasets and show superior performance over current state-of-the-art algorithms. Specifically, for ILIDS-VID, a recent large scale re-id dataset, RSM shows significant improvement over all published approaches, achieving an 11.5% (absolute) improvement in rank 1 accuracy over the closest competing algorithm considered.

Includes members:
  Member "robust bayesian method for simultaneous block sparse signal recovery with applications to face recognition":
    also connected through other hyperedges:
      (none)
  Member "a comprehensive evaluation and benchmark for person re identification features metrics and datasets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "metric attack and defense for person re identification")
  Member "deep recurrent convolutional networks for video based person re identification an end to end approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scan self and collaborative attention network for video person re identification")
  Member "top push video based person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "few shot deep adversarial learning for video based person re identification")
  Member "type i and type ii bayesian methods for sparse signal recovery using scale mixtures":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a gamp based low complexity sparse bayesian learning algorithm")
  Member "person re identification by local maximal occurrence representation and metric learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")
  Member "robust face recognition via block sparse bayesian learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rectified gaussian scale mixtures and the sparse non negative least squares problem")
  Member "sparsity and robustness in face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "connections between nuclear norm and frobenius norm based representations")

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)