human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "analog to digital compression a new paradigm for converting signals to bits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  Member "remote estimation of the wiener process over a channel with random delay":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "who should google scholar update more often")
  Member "compress and estimate in multiterminal source coding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gaussian approximation of quantization error for estimation from compressed data")
  Member "fundamental distortion limits of analog to digital compression":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gaussian approximation of quantization error for estimation from compressed data")
  Member "distortion rate function of sub nyquist sampled gaussian sources":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "analog to digital compression a new paradigm for converting signals to bits")
  Member "sampling rate distortion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "correlated bandits or how to minimize mean squared error online")
  Member "the distortion rate function of cyclostationary gaussian processes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the rate distortion function of asynchronously sampled memoryless cyclostationary gaussian processes")

Question: Please tell me which class the source paper 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)