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 "order optimal consensus through randomized path averaging"):
  Abstract: Gossip algorithms have recently received significant attention, mainly because they constitute simple and robust message-passing schemes for distributed information processing over networks. However for many topologies that are realistic for wireless ad-hoc and sensor networks (like grids and random geometric graphs), the standard nearest-neighbor gossip converges as slowly as flooding ($O(n^2)$ messages). #R##N#A recently proposed algorithm called geographic gossip improves gossip efficiency by a $\sqrt{n}$ factor, by exploiting geographic information to enable multi-hop long distance communications. In this paper we prove that a variation of geographic gossip that averages along routed paths, improves efficiency by an additional $\sqrt{n}$ factor and is order optimal ($O(n)$ messages) for grids and random geometric graphs. #R##N#We develop a general technique (travel agency method) based on Markov chain mixing time inequalities, which can give bounds on the performance of randomized message-passing algorithms operating over various graph topologies.

Includes members:
  Member "location aided fast distributed consensus in wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "convergence speed of the consensus algorithm with interference and sparse long range connectivity")
  Member "gossip algorithms for distributed signal processing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "inverse graph learning over optimization networks")
  Member "distributed consensus algorithms in sensor networks link failures and channel noise":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multitask diffusion adaptation over networks with common latent representations")
  Member "geographic gossip efficient aggregation for sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey of distributed data aggregation algorithms")
  Member "gossiping with multiple messages":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stability and scalability of blockchain systems")
  Member "information dissemination via gossip applications to averaging and coding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gossip algorithms for distributed signal processing")

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)