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: "a review of feature selection methods based on mutual information"
  Abstract: In this work, we present a review of the state of the art of information-theoretic feature selection methods. The concepts of feature relevance, redundance, and complementarity (synergy) are clearly defined, as well as Markov blanket. The problem of optimal feature selection is defined. A unifying theoretical framework is described, which can retrofit successful heuristic criteria, indicating the approximations made by each method. A number of open problems in the field are presented.

Connected through hyperedge HE-1 (induced by source paper "geometric estimation of multivariate dependency"):
  with members:
    - "a dimension independent discriminant between distributions"
    - "convergence rates for empirical estimation of binary classification bounds"
    - "scalable mutual information estimation using dependence graphs"
    - "direct estimation of information divergence using nearest neighbor ratios"
    - "information theoretic structure learning with confidence"
    - "ensemble estimation of mutual information"
    - "improving convergence of divergence functional ensemble estimators"
    - "efficient estimation of mutual information for strongly dependent variables"

Connected through hyperedge HE-2 (induced by source paper "feature selection and feature extraction in pattern analysis a literature review"):
  with members:
    - "pixel level alignment of facial images for high accuracy recognition using ensemble of patches"
    - "graying the black box understanding dqns"
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "a feature subset selection algorithm automatic recommendation method"

Connected through hyperedge HE-3 (induced by source paper "an enhanced ad event prediction method based on feature engineering"):
  with members:
    - "click through rate prediction for contextual advertisment using linear regression"
    - "deep learning over multi field categorical data a case study on user response prediction"
    - "wide deep learning for recommender systems"
    - "deep ctr prediction in display advertising"
    - "sequential click prediction for sponsored search with recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "feature selection via mutual information new theoretical insights"):
  with members:
    - "estimating mutual information for discrete continuous mixtures"

Connected through hyperedge HE-5 (induced by source paper "understanding convolutional neural networks with information theory an initial exploration"):
  with members:
    - "adaptive estimators show information compression in deep neural networks"
    - "understanding individual neuron importance using information theory"
    - "understanding autoencoders with information theoretic concepts"
    - "estimating information flow in neural networks"
    - "simple stopping criteria for information theoretic feature selection"
    - "emergence of invariance and disentanglement in deep representations"
    - "thinet a filter level pruning method for deep neural network compression"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-6 (induced by source paper "computing the unique information"):
  with members:
    - "disentangled representations via synergy minimization"
    - "quantifying morphological computation based on an information decomposition of the sensorimotor loop"
    - "the blackwell relation defines no lattice"
    - "reconsidering unique information towards a multivariate information decomposition"
    - "quantifying unique information"
    - "quantifying synergistic mutual information"
    - "bivariate measure of redundant information"
    - "nonnegative decomposition of multivariate information"

Connected through hyperedge HE-7 (induced by source paper "simple stopping criteria for information theoretic feature selection"):
  with members:
    - "multivariate extension of matrix based renyi s alpha order entropy functional"
    - "deeppink reproducible feature selection in deep neural networks"
    - "challenges of feature selection for big data analytics"
    - "measures of entropy from data using infinitely divisible kernels"
    - "resampling methods for parameter free and robust feature selection with mutual information"

Connected through hyperedge HE-8 (induced by source paper "feature dimensionality reduction for video affect classification a comparative study"):
  with members:
    - "feature selection a data perspective"
    - "eeg based user reaction time estimation using riemannian geometry features"

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.LG(Machine Learning)