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 detection over noisy networks large deviations analysis"):
  Abstract: We study the large deviations performance of consensus+innovations distributed detection over noisy networks, where agents at a time step cooperate with their immediate neighbors (consensus) and assimilate their new observations (innovation.) We show that, under noisy communication, all agents can still achieve an exponential error rate, even when certain (or most) agents cannot detect the event of interest in isolation. The key to achieving this is the appropriate design of the time-varying weight sequence {αk = b0/(a + k)} by which agents weigh their neighbors' messages. We and a communication payoff threshold on the communication noise power, i.e., the critical noise power below which cooperation among neighbors improves detection performance and above which the noise in the communication among agents overwhelms the distributed detector performance. Numerical examples illustrate several tradeoffs among network parameters and between the time (or number of measurements) needed for a reliable decision and the transmission power invested by the agents.

Includes members:
  Member "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "supervised learning under distributed features")
  Member "distributed detection over time varying networks large deviations analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "large deviations performance of consensus innovations distributed detection with non gaussian observations")
  Member "weight optimization for consensus algorithms with correlated switching topology":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online resource inference in network utility maximization problems")
  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 "topology for distributed inference on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed detection in ad hoc networks through quantized consensus")

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)