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 rate distortion risk in estimation from compressed data"
  Abstract: We consider the problem of estimating a latent signal from a lossy compressed version of the data. We assume that the data is generated by an underlying signal and compressed using a lossy compression scheme that is agnostic to this signal. In reconstruction, the underlying signal is estimated so as to minimize a prescribed loss measure. For the above setting and an arbitrary distortion measure between the data and its compressed version, we define the rate-distortion (RD) risk of an estimator as its risk with respect to the distribution achieving Shannon's RD function with respect to this distortion. We derive conditions under which the RD risk describes the risk in estimating from the compressed data. The main theoretical tools to obtain these conditions are transportation-cost inequalities in conjunction with properties of source codes achieving Shannon's RD function. We show that these conditions hold in various settings, including settings where the alphabet of the underlying signal is finite or when the RD achieving distribution is multivariate normal. We evaluate the RD risk in special cases under these settings. This risk provides an achievable loss in compress-and-estimate settings, i.e., when the data is first compressed, communicated or stored using a procedure that is agnostic to the underlying signal, which is later estimated from the compressed version of the data. Our results imply the following general procedure for designing estimators from datasets undergoing lossy compression without specifying the actual compression technique; train the estimator based on a perturbation of the data according to the RD achieving distribution. Under general conditions, this estimator achieves the RD risk when applied to the lossy compressed version of the data.

Connected through hyperedge HE-1 (induced by source paper "gaussian approximation of quantization error for estimation from compressed data"):
  with members:
    - "mean estimation from one bit measurements"
    - "learning distributions from their samples under communication constraints"
    - "refined asymptotics for rate distortion using gaussian codebooks for arbitrary sources"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "the distortion rate function of sampled wiener processes"
    - "compress and estimate in multiterminal source coding"
    - "a universal coding scheme for remote generation of continuous random variables"
    - "fundamental distortion limits of analog to digital compression"

Connected through hyperedge HE-2 (induced by source paper "information theoretic principles of universal discrete denoising"):
  with members:
    - "universal compressed sensing"
    - "deducing truth from correlation"
    - "tensor decompositions for learning latent variable models"
    - "algorithms for discrete denoising under channel uncertainty"
    - "universal minimax discrete denoising under channel uncertainty"

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)