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 "social learning with questions"):
  Abstract: This work studies sequential social learning (also known as Bayesian observational learning), and how private communication can enable agents to avoid herding to the wrong action/state. Starting from the seminal BHW (Bikhchandani, Hirshleifer, and Welch, 1992) model where asymptotic learning does not occur, we allow agents to ask private and finite questions to a bounded subset of their predecessors. While retaining the publicly observed history of the agents and their Bayes rationality from the BHW model, we further assume that both the ability to ask questions and the questions themselves are common knowledge. Then interpreting asking questions as partitioning information sets, we study whether asymptotic learning can be achieved with finite capacity questions. Restricting our attention to the network where every agent is only allowed to query her immediate predecessor, an explicit construction shows that a 1-bit question from each agent is enough to enable asymptotic learning.

Includes members:
  Member "informational braess paradox the effect of information on traffic congestion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "signaling in bayesian network congestion games the subtle power of symmetry")
  Member "social learning with endogenous network formation":
    also connected through other hyperedges:
      (none)
  Member "when is a network epidemic hard to eliminate":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient containment of exact sir markovian processes on networks")
  Member "hypothesis testing in feedforward networks with broadcast failures":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a graphical evolutionary game approach to social learning")
  Member "on learning with finite memory":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed learning for low latency machine type communication in a massive internet of things")
  Member "strategic learning and the topology of social networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fake news in 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)