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 "generalized geometric programming for rate allocation in consensus"):
  Abstract: Distributed averaging, or distributed average consensus, is a common method for computing the sample mean of the data dispersed among the nodes of a network in a decentralized manner. By iteratively exchanging messages with neighbors, the nodes of the network can converge to an agreement on the sample mean of their initial states. In real-world scenarios, these messages are subject to bandwidth and power constraints, which motivates the design of a lossy compression strategy. Few prior works consider the rate allocation problem from the perspective of constrained optimization, which provides a principled method for the design of lossy compression schemes, allows for the relaxation of certain assumptions, and offers performance guarantees. We show for Gaussian-distributed initial states with entropy-coded scalar quantization and vector quantization that the coding rates for distributed averaging can be optimized through generalized geometric programming. In the absence of side information from past states, this approach finds a rate allocation over nodes and iterations that minimizes the aggregate coding rate required to achieve a target mean square error within a finite run time. Our rate allocation is compared to some of the prior art through numerical simulations. The results motivate the incorporation of side-information through differential or predictive coding to improve rate-distortion performance.

Includes members:
  Member "an overview of multi processor approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "source coding optimization for distributed average consensus")
  Member "optimal trade offs in multi processor approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an approximate message passing framework for side information")
  Member "cloud k svd a collaborative dictionary learning algorithm for big distributed data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "byrdie byzantine resilient distributed coordinate descent for decentralized learning")
  Member "performance 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 "toward resource optimal consensus over the wireless medium":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi scale spectrum sensing in dense multi cell cognitive networks")
  Member "non asymptotic analysis of an optimal algorithm for network constrained averaging with noisy links":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "source coding optimization for distributed average consensus")
  Member "gossip algorithms for distributed signal processing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "inverse graph learning over optimization networks")
  Member "distributed lossy averaging":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the interactive communication cost of the distributed nearest lattice point problem")

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)