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 distortion rate function of sampled wiener processes"
  Abstract: The minimal distortion attainable in recovering the waveform of a continuous-time Wiener process from an encoded version of its uniform samples is considered. We first introduce a combined sampling and source coding problem for the Wiener process under uniform sampling, and prove an associated source coding theorem that characterizes the minimal distortion from samples. We then derive a closed-form expression for this minimal distortion, given in terms of a reverse waterfilling expression involving the asymptotic eigenvalue density of a sequence of finite-rank operators. This expression allows to determine the minimal distortion attainable for any prescribed sampling rate and number of bits per time unit. In addition, it can be used to determine the ratio between bitrate and sampling rate when the distortion due to sampling is of the same magnitude as the distortion due to lossy compression. For example, we show that using approximately $1$ bit per sample, the distortion is about $1.12$ times that of the distortion-rate function of the Wiener process at the same bitrate. We also derive an upper bound on the minimal distortion, that is obtained by a sub-optimal compress-and-estimate remote source coding scheme. This bound implies that as the sampling rate goes to infinity, compress-and-estimate is sub-optimal up to a linear factor in the sampling interval.

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"
    - "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"
    - "the rate distortion risk in estimation from compressed data"

Connected through hyperedge HE-2 (induced by source paper "optimal causal rate constrained sampling of the wiener process"):
  with members:
    - "remote estimation of the wiener process over a channel with random delay"
    - "event triggered stabilization of linear systems under bounded bit rates"
    - "infinite horizon optimal transmission power control for remote state estimation over fading channels"
    - "rate cost tradeoffs in control"
    - "optimal estimation via nonanticipative rate distortion function and applications to time varying gauss markov processes"
    - "event triggered control under time varying rates and channel blackouts"
    - "optimal estimation with limited measurements and noisy communication"
    - "optimal strategies for communication and remote estimation with an energy harvesting sensor"

Connected through hyperedge HE-3 (induced by source paper "analog to digital compression a new paradigm for converting signals to bits"):
  with members:
    - "fundamental distortion limits of analog to digital compression"
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "the distortion rate function of cyclostationary gaussian processes"
    - "channel capacity under sub nyquist nonuniform sampling"
    - "speech recognition with deep recurrent neural networks"
    - "randomized algorithms for matrices and data"

Connected through hyperedge HE-4 (induced by source paper "sampling of the wiener process for remote estimation over a channel with random delay"):
  with members:
    - "the dispersion of the gauss markov source"
    - "remote estimation over a packet drop channel with markovian state"
    - "scheduling status updates to minimize age of information with an energy harvesting sensor"
    - "age optimal information updates in multihop networks"
    - "remote estimation of the wiener process over a channel with random delay"
    - "update or wait how to keep your data fresh"
    - "optimizing data freshness throughput and delay in multi server information update systems"
    - "the age of information real time status updating by multiple sources"

Connected through hyperedge HE-5 (induced by source paper "the rate distortion risk in estimation from compressed data"):
  with members:
    - "optimality guarantees for distributed statistical estimation"
    - "a connection between good rate distortion codes and backward dmcs"
    - "mismatched codebooks and the role of entropy coding in lossy data compression"

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)