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: "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.

Connected through hyperedge HE-1 (induced by source paper "information theoretic feature transformation learning for brain interfaces"):
  with members:
    - "adversarial deep learning in eeg biometrics"
    - "hierarchical graphical models for context aware hybrid brain machine interfaces"
    - "transfer learning in brain computer interfaces with adversarial variational autoencoders"
    - "estimating mutual information for discrete continuous mixtures"
    - "information theoretic semi supervised metric learning via entropy regularization"

Connected through hyperedge HE-2 (induced by source paper "multiscale principle of relevant information for hyperspectral image classification"):
  with members:
    - "hyperspectral image classification with markov random fields and a convolutional neural network"
    - "opening the black box of deep neural networks via information"
    - "spatial aware dictionary learning for hyperspectral image classification"

Connected through hyperedge HE-3 (induced by source paper "simple stopping criteria for information theoretic feature selection"):
  with members:
    - "deeppink reproducible feature selection in deep neural networks"
    - "challenges of feature selection for big data analytics"
    - "a review of feature selection methods based on mutual information"
    - "measures of entropy from data using infinitely divisible kernels"
    - "resampling methods for parameter free and robust feature selection with mutual information"

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.IT(Information Theory)