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: "compress and estimate in multiterminal source coding"
  Abstract: We consider a multiterminal remote source coding problem in which a source sequence is estimated from the output of multiple source encoders, each having access only to a noisy observation of the source realization. Each remote encoder compresses its noisy observation sequence so as to minimize a local distortion measure which depends only on the distribution of its observed sequence, and is otherwise independent from the distribution of the underlying source. The latter is estimated at a central location from the output of each of the remote encoders. This source compression and estimation scenario leads to an achievable scheme for the remote multiterminal source coding problem which we term the "compress-and-estimate" (CE) scheme. For the case of a source with independently and identically distributed (i.i.d) elements observed through multiple memoryless channels, we derive a single-letter expression for the distortion in the CE scheme, which we refer to as the CE distortion-rate function (CE-DRF). We prove that the CE-DRF can be achieved by estimating the source realization from the output of any set of encoders, as long as each encoder attains its local rate-distortion function. We prove in addition a converse result saying that, for large enough blocklength, the distortion in estimating a finite sub-block of the source from the output of such encoders, averaged over all sub-blocks, does not exceed the CE-DRF. Finally, we derive closed-form expressions for the CE-DRF in the case of a Gaussian source observed through multiple AWGN channels under quadratic distortion, and for the case of a binary source observed through multiple biflip channels under Hamming distortion.

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"
    - "a universal coding scheme for remote generation of continuous random variables"
    - "fundamental distortion limits of analog to digital compression"
    - "the rate distortion risk in estimation from compressed data"

Connected through hyperedge HE-2 (induced by source paper "compress and estimate source coding for a vector gaussian source"):
  with members:
    - "source coding in networks with covariance distortion constraints"

Connected through hyperedge HE-3 (induced by source paper "the distortion rate function of sampled wiener processes"):
  with members:
    - "analog to digital compression a new paradigm for converting signals to bits"
    - "remote estimation of the wiener process over a channel with random delay"
    - "fundamental distortion limits of analog to digital compression"
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "sampling rate distortion"
    - "the distortion rate function of cyclostationary gaussian processes"

Connected through hyperedge HE-4 (induced by source paper "the distortion rate function of cyclostationary gaussian processes"):
  with members:
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "shannon meets nyquist capacity of sampled gaussian channels"

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)