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: "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.

Connected through hyperedge HE-1 (induced by source paper "collaborative information bottleneck"):
  with members:
    - "distributed binary detection with lossy data compression"
    - "the three terminal interactive lossy source coding problem"
    - "deep learning with elastic averaging sgd"
    - "representation learning a review and new perspectives"
    - "multiterminal source coding under logarithmic loss"
    - "information theoretic proofs of entropy power inequalities"
    - "cascade triangular and two way source coding with degraded side information at the second user"
    - "two way source coding with a helper"

Connected through hyperedge HE-2 (induced by source paper "collaborative representation learning"):
  with members:
    - "the three terminal interactive lossy source coding problem"
    - "deep learning with elastic averaging sgd"
    - "representation learning a review and new perspectives"
    - "information theoretic proofs of entropy power inequalities"
    - "cascade triangular and two way source coding with degraded side information at the second user"
    - "two way source coding with a helper"
    - "rate region of the quadratic gaussian two encoder source coding problem"

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.IT(Information Theory)