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 "toward resource optimal consensus over the wireless medium"):
  Abstract: We carry out a comprehensive study of the resource cost of averaging consensus in wireless networks. Most previous approaches suppose a graphical network, which abstracts away crucial features of the wireless medium, and measure resource consumption only in terms of the total number of transmissions required to achieve consensus. Under a path-loss model, we study the resource requirements of consensus with respect to three wireless-appropriate metrics: total transmit energy, elapsed time, and time-bandwidth product. First, we characterize the performance of several popular gossip algorithms, showing that they may be order-optimal with respect to transmit energy but are strictly suboptimal with respect to elapsed time and time-bandwidth product. Further, we propose a new consensus scheme, termed hierarchical averaging, and show that it is nearly order-optimal with respect to all three metrics. Finally, we examine the effects of quantization, showing that hierarchical averaging provides a nearly order-optimal tradeoff between resource consumption and quantization error.

Includes members:
  Member "convergence speed of the consensus algorithm with interference and sparse long range connectivity":
    also connected through other hyperedges:
      (none)
  Member "capacity scaling of wireless ad hoc networks shannon meets maxwell":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "network decomposed hierarchical cooperation in ad hoc networks with social relationships")
  Member "multiscale gossip for efficient decentralized averaging in wireless packet networks":
    also connected through other hyperedges:
      (none)
  Member "geographic gossip efficient averaging for sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "quantifying differential privacy of gossip protocols in general networks")
  Member "order optimal consensus through randomized path averaging":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi scale spectrum sensing in dense multi cell cognitive networks")
  Member "hierarchical cooperation achieves optimal capacity scaling in ad hoc networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "wireless mesh networking with devices equipped with multi connectivity")

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)