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 "simple stopping criteria for information theoretic feature selection"):
  Abstract: Feature selection aims to select the smallest feature subset that yields the minimum generalization error. In the rich literature in feature selection, information theory-based approaches seek a subset of features such that the mutual information between the selected features and the class labels is maximized. Despite the simplicity of this objective, there still remain several open problems in optimization. These include, for example, the automatic determination of the optimal subset size (i.e., the number of features) or a stopping criterion if the greedy searching strategy is adopted. In this paper, we suggest two stopping criteria by just monitoring the conditional mutual information (CMI) among groups of variables. Using the recently developed multivariate matrix-based Renyi's \alpha-entropy functional, which can be directly estimated from data samples, we showed that the CMI among groups of variables can be easily computed without any decomposition or approximation, hence making our criteria easy to implement and seamlessly integrated into any existing information theoretic feature selection methods with a greedy search strategy.

Includes members:
  Member "multivariate extension of matrix based renyi s alpha order entropy functional":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "information theoretic feature transformation learning for brain interfaces")
  Member "deeppink reproducible feature selection in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust saliency maps with decoy enhanced saliency score")
  Member "challenges of feature selection for big data analytics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "causality based feature selection methods and evaluations")
  Member "a review of feature selection methods based on mutual information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "geometric estimation of multivariate dependency")
  Member "measures of entropy from data using infinitely divisible kernels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "understanding autoencoders with information theoretic concepts")
  Member "resampling methods for parameter free and robust feature selection with mutual information":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "feature relevance determination for ordinal regression in the context of feature redundancies and privileged information")

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)