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 "source coding optimization for distributed average consensus"):
  Abstract: Consensus is a common method for computing a function of the data distributed among the nodes of a network. Of particular interest is distributed average consensus, whereby the nodes iteratively compute the sample average of the data stored at all the nodes of the network using only near-neighbor communications. In real-world scenarios, these communications must undergo quantization, which introduces distortion to the internode messages. In this thesis, a model for the evolution of the network state statistics at each iteration is developed under the assumptions of Gaussian data and additive quantization error. It is shown that minimization of the communication load in terms of aggregate source coding rate can be posed as a generalized geometric program, for which an equivalent convex optimization can efficiently solve for the global minimum. Optimization procedures are developed for rate-distortion-optimal vector quantization, uniform entropy-coded scalar quantization, and fixed-rate uniform quantization. Numerical results demonstrate the performance of these approaches. For small numbers of iterations, the fixed-rate optimizations are verified using exhaustive search. Comparison to the prior art suggests competitive performance under certain circumstances but strongly motivates the incorporation of more sophisticated coding strategies, such as differential, predictive, or Wyner-Ziv coding.

Includes members:
  Member "performance limits for noisy multimeasurement vector problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exploiting prior information in block sparse signals")
  Member "an overview of multi processor approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generalized geometric programming for rate allocation in consensus")
  Member "statistical physics and information theory perspectives on linear inverse problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "performance limits with additive error metrics in noisy multimeasurement vector problems")
  Member "optimal trade offs in multi processor approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an approximate message passing framework for side information")
  Member "multi processor approximate message passing using lossy compression":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "statistical physics and information theory perspectives on linear inverse problems")
  Member "distributed parameter estimation with quantized communication via running average":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "detection under one bit messaging over adaptive networks")
  Member "on constrained randomized quantization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ratq a universal fixed length quantizer for stochastic optimization")
  Member "toward resource optimal consensus over the wireless medium":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi scale spectrum sensing in dense multi cell cognitive networks")

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)