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 "optimal causal rate constrained sampling of the wiener process"):
  Abstract: We consider the following communication scenario. An encoder causally observes the Wiener process and decides when and what to transmit about it. A decoder makes real-time estimation of the process using causally received codewords. We determine the causal encoding and decoding policies that jointly minimize the mean-square estimation error, under the long-term communication rate constraint of $R$ bits per second. We show that an optimal encoding policy can be implemented as a causal sampling policy followed by a causal compressing policy. We prove that the optimal encoding policy samples the Wiener process once the innovation passes either $\sqrt{\frac{1}{R}}$ or $-\sqrt{\frac{1}{R}}$, and compresses the sign of the innovation (SOI) using a 1-bit codeword. The SOI coding scheme achieves the operational distortion-rate function, which is equal to $D^{\mathrm{op}}(R)=\frac{1}{6R}$. Surprisingly, this is significantly better than the distortion-rate tradeoff achieved in the limit of infinite delay by the best non-causal code. This is because the SOI coding scheme leverages the free timing information supplied by the zero-delay channel between the encoder and the decoder. The key to unlock that gain is the event-triggered nature of the SOI sampling policy. In contrast, the distortion-rate tradeoffs achieved with deterministic sampling policies are much worse: we prove that the causal informational distortion-rate function in that scenario is as high as $D_{\mathrm{DET}}(R) = \frac{5}{6R}$. It is achieved by the uniform sampling policy with the sampling interval $\frac{1}{R}$. In either case, the optimal strategy is to sample the process as fast as possible and to transmit 1-bit codewords to the decoder without delay.

Includes members:
  Member "remote estimation of the wiener process over a channel with random delay":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "who should google scholar update more often")
  Member "event triggered stabilization of linear systems under bounded bit rates":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal causal rate constrained sampling for a class of continuous markov processes")
  Member "infinite horizon optimal transmission power control for remote state estimation over fading channels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal scheduling of multiple sensors over lossy and bandwidth limited channels")
  Member "the distortion rate function of sampled wiener processes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gaussian approximation of quantization error for estimation from compressed data")
  Member "rate cost tradeoffs in control":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "leveraging linear quadratic regulator cost and energy consumption for ultra reliable and low latency iot control systems")
  Member "optimal estimation via nonanticipative rate distortion function and applications to time varying gauss markov processes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a converse bound characterization on zero delay indirect rdf for vector gauss markov sources")
  Member "event triggered control under time varying rates and channel blackouts":
    also connected through other hyperedges:
      (none)
  Member "optimal estimation with limited measurements and noisy communication":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "can we achieve fresh information with selfish users in mobile crowd learning")

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)