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: "discovering structure in high dimensional data through correlation explanation"
  Abstract: We introduce a method to learn a hierarchy of successively more abstract representations of complex data based on optimizing an information-theoretic objective. Intuitively, the optimization searches for a set of latent factors that best explain the correlations in the data as measured by multivariate mutual information. The method is unsupervised, requires no model assumptions, and scales linearly with the number of variables which makes it an attractive approach for very high dimensional systems. We demonstrate that Correlation Explanation (CorEx) automatically discovers meaningful structure for data from diverse sources including personality tests, DNA, and human language.

Connected through hyperedge HE-1 (induced by source paper "correlation congruence for knowledge distillation"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "the devil of face recognition is in the noise"
    - "knowledge distillation with adversarial samples supporting decision boundary"
    - "arcface additive angular margin loss for deep face recognition"
    - "large scale distributed neural network training through online distillation"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "deep mutual learning"

Connected through hyperedge HE-2 (induced by source paper "efficient covariance estimation from temporal data"):
  with members:
    - "network inference via the time varying graphical lasso"
    - "adam a method for stochastic optimization"
    - "inverse covariance estimation for high dimensional data in linear time and space spectral methods for riccati and sparse models"

Connected through hyperedge HE-3 (induced by source paper "an information theoretic approach to unsupervised feature selection for high dimensional data"):
  with members:
    - "an information theoretic interpretation to deep neural networks"
    - "on universal features for high dimensional learning and inference"
    - "linear information coupling problems"

Connected through hyperedge HE-4 (induced by source paper "discovering reliable correlations in categorical data"):
  with members:
    - "discovering reliable dependencies from data hardness and improved algorithms"
    - "discovering reliable approximate functional dependencies"

Connected through hyperedge HE-5 (induced by source paper "kernelized hashcode representations for relation extraction"):
  with members:
    - "learning to explain an information theoretic perspective on model interpretation"
    - "deep learning for extracting protein protein interactions from biomedical literature"
    - "deep variational information bottleneck"
    - "a survey on learning to hash"
    - "orthogonal random features"
    - "generalized exponential concentration inequality for r enyi divergence estimation"
    - "learning structural kernels for natural language processing"
    - "adaptive estimation of shannon entropy"

Connected through hyperedge HE-6 (induced by source paper "info clustering a mathematical theory for data clustering"):
  with members:
    - "bounds on the communication rate needed to achieve sk capacity in the hypergraphical source model"
    - "duality between feature selection and data clustering"
    - "fairness in communication for omniscience"
    - "incremental and decremental secret key agreement"
    - "on the public communication needed to achieve sk capacity in the multiterminal source model"
    - "iterative merging algorithm for cooperative data exchange"
    - "deep residual learning for image recognition"
    - "the communication complexity of achieving sk capacity in a class of pin models"

Connected through hyperedge HE-7 (induced by source paper "demystifying fixed k nearest neighbor information estimators"):
  with members:
    - "ensemble estimation of mutual information"
    - "improving convergence of divergence functional ensemble estimators"
    - "equivalence of additive combinatorial linear inequalities for shannon entropy and differential entropy"
    - "generalized exponential concentration inequality for r enyi divergence estimation"
    - "nonparametric ensemble estimation of distributional functionals"
    - "on additive combinatorial affine inequalities for shannon entropy and differential entropy"
    - "a non parametric k nearest neighbour entropy estimator"
    - "justification of logarithmic loss via the benefit of side information"

Connected through hyperedge HE-8 (induced by source paper "latent tree models for hierarchical topic detection"):
  with members:
    - "topic browsing for research papers with hierarchical latent tree analysis"
    - "sparse boltzmann machines with structure learning as applied to text analysis"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "a survey on latent tree models and applications"
    - "a method of moments for mixture models and hidden markov models"
    - "latent tree models and approximate inference in bayesian networks"
    - "learning nonsingular phylogenies and hidden markov models"

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)