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 "a distributed learning dynamics in social groups"):
  Abstract: We study a distributed learning process observed in human groups and other social animals. This learning process appears in settings in which each individual in a group is trying to decide over time, in a distributed manner, which option to select among a shared set of options. Specifically, we consider a stochastic dynamics in a group in which every individual selects an option in the following two-step process: (1) select a random individual and observe the option that individual chose in the previous time step, and (2) adopt that option if its stochastic quality was good at that time step. Various instantiations of such distributed learning appear in nature, and have also been studied in the social science literature. From the perspective of an individual, an attractive feature of this learning process is that it is a simple heuristic that requires extremely limited computational capacities. But what does it mean for the group -- could such a simple, distributed and essentially memoryless process lead the group as a whole to perform optimally? We show that the answer to this question is yes -- this distributed learning is highly effective at identifying the best option and is close to optimal for the group overall. Our analysis also gives quantitative bounds that show fast convergence of these stochastic dynamics. Prior to our work the only theoretical work related to such learning dynamics has been either in deterministic special cases or in the asymptotic setting. Finally, we observe that our infinite population dynamics is a stochastic variant of the classic multiplicative weights update (MWU) method. Consequently, we arrive at the following interesting converse: the learning dynamics on a finite population considered here can be viewed as a novel distributed and low-memory implementation of the classic MWU method.

Includes members:
  Member "ant inspired density estimation via random walks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed community detection via metastability of the 2 choices dynamics")
  Member "human collective intelligence as distributed bayesian inference":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "social learning in multi agent multi armed bandits")
  Member "irls and slime mold equivalence and convergence":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stronger l2 l2 compressed sensing without iterating")
  Member "distributed house hunting in ant colonies":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed community detection via metastability of the 2 choices dynamics")
  Member "stabilizing consensus with many opinions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the necessary memory to compute the plurality in multi agent systems")
  Member "distributed algorithms for learning and cognitive medium access with logarithmic regret":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "my fair bandit distributed learning of max min fairness with multi player bandits")
  Member "decentralized online learning algorithms for opportunistic spectrum access":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robust stochastic bandit algorithms under probabilistic unbounded adversarial attack")
  Member "online distributed sensor selection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "periodic bandits and wireless network selection")

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.LG(Machine Learning)