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: "the non regular ceo problem"
  Abstract: We consider the CEO problem for non-regular source distributions (such as uniform or truncated Gaussian). A group of agents observe independently corrupted versions of data and transmit coded versions over rate-limited links to a CEO. The CEO then estimates the underlying data based on the received coded observations. Agents are not allowed to convene before transmitting their observations. This formulation is motivated by the practical problem of a firm's CEO estimating (non-regular) beliefs about a sequence of events, before acting on them. Agents' observations are modeled as jointly distributed with the underlying data through a given conditional probability density function. We study the asymptotic behavior of the minimum achievable mean squared error distortion at the CEO in the limit when the number of agents $L$ and the sum rate $R$ tend to infinity. We establish a $1/R^2$ convergence of the distortion, an intermediate regime of performance between the exponential behavior in discrete CEO problems [Berger, Zhang, and Viswanathan (1996)], and the $1/R$ behavior in Gaussian CEO problems [Viswanathan and Berger (1997)]. Achievability is proved by a layered architecture with scalar quantization, distributed entropy coding, and midrange estimation. The converse is proved using the Bayesian Chazan-Zakai-Ziv bound.

Connected through hyperedge HE-1 (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"
    - "how to achieve the capacity of asymmetric channels"
    - "multiterminal source coding under logarithmic loss"
    - "decentralized coding algorithms for distributed storage in wireless sensor networks"

Connected through hyperedge HE-2 (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"
    - "network compression worst case analysis"
    - "an outer bound for the vector gaussian ceo problem"
    - "multiterminal source coding under logarithmic loss"
    - "successive wyner ziv coding scheme and its application to the quadratic gaussian ceo problem"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-3 (induced by source paper "interactive scalar quantization for distributed resource allocation"):
  with members:
    - "overhead performance tradeoffs a resource allocation perspective"
    - "distributed functional scalar quantization simplified"
    - "distributed function computation over a rooted directed tree"
    - "multiterminal source coding under logarithmic loss"
    - "distributed scalar quantization for computing high resolution analysis and extensions"
    - "interaction strictly improves the wyner ziv rate distortion function"
    - "optimal feedback communication via posterior matching"

Connected through hyperedge HE-4 (induced by source paper "queuing approaches to principal agent communication under information overload"):
  with members:
    - "channel coding the road to channel capacity"

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)