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 "on the information bottleneck problems models connections applications and information theoretic views"):
  Abstract: This tutorial paper focuses on the variants of the bottleneck problem taking an information theoretic perspective and discusses practical methods to solve it, as well as its connection to coding and learning aspects. The intimate connections of this setting to remote source-coding under logarithmic loss distortion measure, information combining, common reconstruction, the Wyner–Ahlswede–Korner problem, the efficiency of investment information, as well as, generalization, variational inference, representation learning, autoencoders, and others are highlighted. We discuss its extension to the distributed information bottleneck problem with emphasis on the Gaussian model and highlight the basic connections to the uplink Cloud Radio Access Networks (CRAN) with oblivious processing. For this model, the optimal trade-offs between relevance (i.e., information) and complexity (i.e., rates) in the discrete and vector Gaussian frameworks is determined. In the concluding outlook, some interesting problems are mentioned such as the characterization of the optimal inputs (“features”) distributions under power limitations maximizing the “relevance” for the Gaussian information bottleneck, under “complexity” constraints.

Includes members:
  Member "learning representations for neural network based classification using the information bottleneck principle":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "machine learning using the variational predictive information bottleneck with a validation set":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "general information bottleneck objectives and their applications to machine learning")
  Member "on the difference between the information bottleneck and the deep information bottleneck":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "inverse learning of symmetry transformations")
  Member "extracting robust and accurate features via a robust information bottleneck":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning adversarially robust representations via worst case mutual information maximization")
  Member "general information bottleneck objectives and their applications to machine learning":
    also connected through other hyperedges:
      (none)
  Member "vector gaussian ceo problem under logarithmic loss":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "vector gaussian ceo problem under logarithmic loss and applications")
  Member "on universal features for high dimensional learning and inference":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an information theoretic interpretation to deep neural networks")
  Member "estimation efficiency under privacy constraints":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "obfuscation via information density estimation")

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)