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 ceo problem with rth power of difference and logarithmic distortions"
  Abstract: The CEO problem has received a lot of attention since Berger et al.~first investigated it, however, there are limited results on non-Gaussian models with non-quadratic distortion measures. In this work, we extend the CEO problem to two continuous alphabet settings with general $r$th power of difference and logarithmic distortions, and study asymptotics of distortion as the number of agents and sum rate grow without bound. The first setting is a regular source-observation model, such as jointly Gaussian, with difference distortion and we show that the distortion decays at $R_{\textsf{sum}}$ up to a multiplicative constant. We use sample median estimation following the Berger-Tung scheme for achievability and the Shannon lower bound for the converse. The other setting is a non-regular source-observation model, such as copula or uniform additive noise models, with difference distortion for which estimation-theoretic regularity conditions do not hold. The optimal decay $R_{\textsf{sum}}$ is obtained for the non-regular model by midrange estimator following the Berger-Tung scheme for achievability and the Chazan-Ziv-Zakai bound for the converse. Lastly, we provide a condition for the regular model, under which quadratic and logarithmic distortions are asymptotically equivalent by entropy power relation as the number of agents grows. This proof relies on the Bernstein-von Mises theorem.

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"
    - "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"
    - "decentralized coding algorithms for distributed storage in wireless sensor networks"

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)