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: "demystifying information theoretic clustering"
  Abstract: We propose a novel method for clustering data which is grounded in information-theoretic principles and requires no parametric assumptions. Previous attempts to use information theory to define clusters in an assumption-free way are based on maximizing mutual information between data and cluster labels. We demonstrate that this intuition suffers from a fundamental conceptual flaw that causes clustering performance to deteriorate as the amount of data increases. Instead, we return to the axiomatic foundations of information theory to define a meaningful clustering measure based on the notion of consistency under coarse-graining for finite data.

Connected through hyperedge HE-1 (induced by source paper "deep unsupervised clustering with gaussian mixture variational autoencoders"):
  with members:
    - "improving variational inference with inverse autoregressive flow"
    - "stochastic backpropagation through mixture density distributions"
    - "attend infer repeat fast scene understanding with generative models"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "tagger deep unsupervised perceptual grouping"
    - "variational lossy autoencoder"
    - "generating sentences from a continuous space"
    - "unsupervised deep embedding for clustering analysis"

Connected through hyperedge HE-2 (induced by source paper "distributed information theoretic biclustering"):
  with members:
    - "which boolean functions are most informative"
    - "evaluation of marton s inner bound for the general broadcast channel"
    - "multiterminal source coding under logarithmic loss"
    - "evaluation of marton s inner bound for the general broadcast channel"

Connected through hyperedge HE-3 (induced by source paper "max entropy feed forward clustering neural network"):
  with members:
    - "bayesian nonparametric multilevel clustering with group level contexts"
    - "unsupervised feature learning by deep sparse coding"
    - "multi column deep neural networks for image classification"

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)