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 "gaussian approximation of quantization error for estimation from compressed data"):
  Abstract: We consider the distributional connection between the lossy compressed representation of a high-dimensional signal $X$ using a random spherical code and the observation of $X$ under an additive white Gaussian noise (AWGN). We show that the Wasserstein distance between a bitrate-$R$ compressed version of $X$ and its observation under an AWGN-channel of signal-to-noise ratio $2^{2R}-1$ is sub-linear in the problem dimension. We utilize this fact to connect the risk of an estimator based on an AWGN-corrupted version of $X$ to the risk attained by the same estimator when fed with its bitrate-$R$ quantized version. We demonstrate the usefulness of this connection by deriving various novel results for inference problems under compression constraints, including noisy source coding and limited-bitrate parameter estimation.

Includes members:
  Member "mean estimation from one bit measurements":
    also connected through other hyperedges:
      (none)
  Member "learning distributions from their samples under communication constraints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "serial quantization for sparse time sequences")
  Member "refined asymptotics for rate distortion using gaussian codebooks for arbitrary sources":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exponential strong converse for content identification with lossy recovery")
  Member "the replica symmetric prediction for compressed sensing with gaussian matrices is exact":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsourced multiuser sparse regression codes achieve the symmetric mac capacity")
  Member "the distortion rate function of sampled wiener processes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal causal rate constrained sampling of the wiener process")
  Member "compress and estimate in multiterminal source coding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compress and estimate source coding for a vector gaussian source")
  Member "a universal coding scheme for remote generation of continuous random variables":
    also connected through other hyperedges:
      (none)
  Member "fundamental distortion limits of analog to digital compression":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hardware limited task based quantization")

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)