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 "distributed information theoretic biclustering"):
  Abstract: This paper investigates the problem of distributed biclustering of memoryless sources and extends previous work [1] to the general case with more than two sources. Given a set of distributed stationary memoryless sources, the encoders' goal is to find rate-limited representations of these sources such that the mutual information between two selected subsets of descriptions (each of them generated by distinct encoders) is maximized. This formulation is fundamentally different from conventional distributed source coding problems since here redundancy among descriptions should actually be maximally preserved. We derive non-trivial outer and inner bounds to the achievable region for this problem and further connect them to the CEO problem under logarithmic loss distortion. Since information-theoretic biclustering is closely related to distributed hypothesis testing against independence, our results are also expected to apply to that problem.

Includes members:
  Member "which boolean functions are most informative":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on universal features for high dimensional learning and inference")
  Member "demystifying information theoretic clustering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep unsupervised clustering with gaussian mixture variational autoencoders")
  Member "evaluation of marton s inner bound for the general broadcast channel":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the direction of discrimination an information theoretic analysis of disparate impact in machine learning")
  Member "multiterminal source coding under logarithmic loss":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  Member "evaluation of marton s inner bound for the general broadcast channel":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the direction of discrimination an information theoretic analysis of disparate impact in machine learning")

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)