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: "successive wyner ziv coding scheme and its application to the quadratic gaussian ceo problem"
  Abstract: In this paper, we introduce a distributed source coding scheme called successive Wyner-Ziv coding. We show that every point in the rate region of the quadratic Gaussian CEO problem can be achieved via successive Wyner-Ziv coding. The concept of successive refinement in single source coding is generalized to the distributed source coding scenario, which we refer to as distributed successive refinement. For the quadratic Gaussian CEO problem, we establish a necessary and sufficient condition for distributed successive refinement, where the successive Wyner-Ziv coding scheme plays an important role.

Connected through hyperedge HE-1 (induced by source paper "fundamental limits of distributed tracking"):
  with members:
    - "rate cost tradeoffs in control"
    - "a characterization of the minimal average data rate that guarantees a given closed loop performance level"
    - "multiterminal source coding under logarithmic loss"
    - "improved upper bounds to the causal quadratic rate distortion function for gaussian stationary sources"
    - "rate region of the quadratic gaussian two encoder source coding problem"

Connected through hyperedge HE-2 (induced by source paper "generalized gaussian multiterminal source coding in the high resolution regime"):
  with members:
    - "robust distributed compression of symmetrically correlated gaussian sources"
    - "generalized gaussian multiterminal source coding the symmetric case"
    - "robust distributed source coding"
    - "rate region of the quadratic gaussian two encoder source coding problem"

Connected through hyperedge HE-3 (induced by source paper "vector gaussian ceo problem under logarithmic loss and applications"):
  with members:
    - "vector gaussian ceo problem under logarithmic loss"
    - "chaining mutual information and tightening generalization bounds"
    - "on the universality of the logistic loss function"
    - "minimax learning for remote prediction"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "distributed information bottleneck method for discrete and gaussian sources"
    - "universality of logarithmic loss in lossy compression"
    - "a generalization of blahut arimoto algorithm to compute rate distortion regions of multiterminal source coding under logarithmic loss"

Connected through hyperedge HE-4 (induced by source paper "successive wyner ziv coding for the binary ceo problem under logarithmic loss"):
  with members:
    - "successive wyner ziv coding for the binary ceo problem under log loss"
    - "analysis and code design for the binary ceo problem under logarithmic loss"
    - "remote source coding under gaussian noise dueling roles of power and entropy power"
    - "the ceo problem with rth power of difference and logarithmic distortions"
    - "on the optimal fronthaul compression and decoding strategies for uplink cloud radio access networks"
    - "the non regular ceo problem"
    - "how to achieve the capacity of asymmetric channels"
    - "multiterminal source coding under logarithmic loss"

Connected through hyperedge HE-5 (induced by source paper "the ceo problem with rth power of difference and logarithmic distortions"):
  with members:
    - "remote source coding under gaussian noise dueling roles of power and entropy power"
    - "universality of logarithmic loss in lossy compression"
    - "the shannon lower bound is asymptotically tight"
    - "the non regular ceo problem"
    - "network compression worst case analysis"
    - "an outer bound for the vector gaussian ceo problem"
    - "multiterminal source coding under logarithmic loss"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-6 (induced by source paper "robust distributed compression of symmetrically correlated gaussian sources"):
  with members:
    - "generalized gaussian multiterminal source coding the symmetric case"
    - "the sum rate of vector gaussian multiple description coding with tree structured covariance distortion constraints"
    - "multiuser successive refinement and multiple description coding"
    - "multiple description quantization via gram schmidt orthogonalization"
    - "robust distributed source coding"
    - "vector gaussian multiple description with individual and central receivers"
    - "rate region of the quadratic gaussian two encoder source coding problem"

Connected through hyperedge HE-7 (induced by source paper "successive wyner ziv coding for the binary ceo problem under log loss"):
  with members:
    - "analysis and code design for the binary ceo problem under logarithmic loss"
    - "multiterminal source coding under logarithmic loss"
    - "distributed source coding of correlated gaussian remote sources"
    - "binary quantization using belief propagation with decimation over factor graphs of ldgm codes"

Connected through hyperedge HE-8 (induced by source paper "binary ceo problem under log loss with bsc test channel model"):
  with members:
    - "analysis and code design for the binary ceo problem under logarithmic loss"
    - "universality of logarithmic loss in lossy compression"
    - "multiterminal source coding under logarithmic loss"
    - "distributed source coding of correlated gaussian remote sources"

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)