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 "distributed inference for relay assisted sensor networks with intermittent measurements over fading channels"):
  Abstract: In this paper, we consider a general distributed estimation problem in relay-assisted sensor networks by taking into account time-varying asymmetric communications, fading channels, and intermittent measurements. Motivated by centralized filtering algorithms, we propose a distributed innovation-based estimation algorithm by combining the measurement innovation (assimilation of new measurement) and local data innovation (incorporation of neighboring data). Our algorithm is fully distributed which does not need a fusion center. We establish theoretical results regarding asymptotic unbiasedness and consistency of the proposed algorithm. Specifically, we utilize an reordering technique and the generalized Perron complement to manipulate the first and second moment analyses in a tractable framework. Furthermore, we present a performance-oriented design of the algorithm parameters for energy-constrained networks based on the theoretical results. Simulation results corroborate the theoretical findings, thus demonstrating the effectiveness of the proposed algorithm.

Includes members:
  Member "toward resource optimal consensus over the wireless medium":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi scale spectrum sensing in dense multi cell cognitive networks")
  Member "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "supervised learning under distributed features")
  Member "the role of persistent graphs in the agreement seeking of social networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a tutorial on modeling and analysis of dynamic social networks part i")
  Member "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed learning in the non convex world from batch to streaming data and beyond")
  Member "sensor networks with random links topology design for distributed consensus":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "matcha speeding up decentralized sgd via matching decomposition sampling")
  Member "distributed decision through self synchronizing sensor networks in the presence of propagation delays and nonreciprocal channels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed detection and estimation in wireless sensor 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)