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 "average error probability in wireless sensor networks with imperfect sensing and communication for different decision rules"):
  Abstract: This paper presents a framework to evaluate the probability that a decision error event occurs in wireless sensor networks, including sensing and communication errors. We consider a scenario where sensors need to identify whether a given event has occurred based on its periodic, noisy, observations of a given signal. Such information about the signal needs to be sent to a fusion center that decides about the actual state at that specific observation time. The communication links -- single- or multi-hop -- are modeled as binary symmetric channels, which may have different error probabilities. The decision at the fusion center is based on OR, AND, K-OUT-OF-N and MAJORITY Boolean operations on the received signals associated to individual sensor observations. We derive closed-form equations for the average decision error probability as a function of the system parameters (e.g. number of sensors and hops) and the input signal characterization. Our analyses show the best decision rule is closely related to the frequency that the observed events occur and the number of sensors. In our numerical example, we show that the AND rule outperforms MAJORITY if such an event is rare and there is only a handful number of sensors. Conversely, if there is a large number of sensors or more evenly distributed event occurrences, the MAJORITY is the best choice. We further show that, while the error probability using the MAJORITY rule asymptotically goes to 0 with increasing number of sensors, it is also more susceptible to higher channel error probabilities.

Includes members:
  Member "robust decentralized detection and social learning in tandem networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an unsupervised bayesian neural network for truth discovery")
  Member "effects of relay selection strategies on the spectral efficiency of wireless systems with half and full duplex nodes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "energy efficiency of opportunistic device to device relaying under lognormal shadowing")
  Member "a survey on internet of things from industrial market perspective":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a throughput and energy efficiency scheme for unlicensed massive machine type communications")
  Member "throughput analysis of cognitive wireless networks with poisson distributed nodes based on location information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "energy efficiency of an unlicensed wireless network in the presence of retransmissions")
  Member "detection performance in balanced binary relay trees with node and link failures":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed inference in tree networks using coding theory")
  Member "error probability bounds for balanced binary relay trees":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "whose opinion to follow in multihypothesis social learning a large deviations perspective")

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)