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 "multivariate extension of matrix based renyi s alpha order entropy functional"):
  Abstract: The matrix-based Renyi's \alpha-order entropy functional was recently introduced using the normalized eigenspectrum of a Hermitian matrix of the projected data in a reproducing kernel Hilbert space (RKHS). However, the current theory in the matrix-based Renyi's \alpha-order entropy functional only defines the entropy of a single variable or mutual information between two random variables. In information theory and machine learning communities, one is also frequently interested in multivariate information quantities, such as the multivariate joint entropy and different interactive quantities among multiple variables. In this paper, we first define the matrix-based Renyi's \alpha-order joint entropy among multiple variables. We then show how this definition can ease the estimation of various information quantities that measure the interactions among multiple variables, such as interactive information and total correlation. We finally present an application to feature selection to show how our definition provides a simple yet powerful way to estimate a widely-acknowledged intractable quantity from data. A real example on hyperspectral image (HSI) band selection is also provided.

Includes members:
  Member "feature selection a data perspective":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "multivariate dependence beyond shannon information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a partial information decomposition based on causal tensors")
  Member "advances in hyperspectral image classification earth monitoring with statistical learning methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "3d quasi recurrent neural network for hyperspectral image denoising")
  Member "measures of entropy from data using infinitely divisible kernels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "understanding autoencoders with information theoretic concepts")
  Member "multivariate information measures an experimentalist s perspective":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient learning of optimal markov network topology with k tree modeling")
  Member "nonnegative decomposition of multivariate information":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an operational information decomposition via synergistic disclosure")
  Member "quantifying and visualizing attribute interactions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "measuring multivariate redundant information with pointwise common change in surprisal")

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.IT(Information Theory)