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 "asynchronous majority dynamics in preferential attachment trees"):
  Abstract: We study information aggregation in networks where agents make binary decisions (labeled incorrect or correct). Agents initially form independent private beliefs about the better decision, which is correct with probability $1/2+\delta$. The dynamics we consider are asynchronous (each round, a single agent updates their announced decision) and non-Bayesian (agents simply copy the majority announcements among their neighbors, tie-breaking in favor of their private signal). #R##N#Our main result proves that when the network is a tree formed according to the preferential attachment model~\cite{BarabasiA99}, with high probability, the process stabilizes in a correct majority. We extend our results to other tree structures, including balanced $M$-ary trees for any $M$.

Includes members:
  Member "stabilizing consensus with many opinions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the necessary memory to compute the plurality in multi agent systems")
  Member "reaching consensus via non bayesian asynchronous learning in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "resource allocation game on social networks best response dynamics and convergence")
  Member "learning in hierarchical social networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the relationship between inference and data privacy in decentralized iot networks")
  Member "subexponential convergence for information aggregation on regular trees":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning in hierarchical social 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.GT(Computer Science and Game Theory)