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 in multi agent multi armed bandits"):
  Abstract: In this paper, we introduce a distributed version of the classical stochastic Multi-Arm Bandit (MAB) problem. Our setting consists of a large number of agents $n$ that collaboratively and simultaneously solve the same instance of $K$ armed MAB to minimize the average cumulative regret over all agents. The agents can communicate and collaborate among each other \emph{only} through a pairwise asynchronous gossip based protocol that exchange a limited number of bits. In our model, agents at each point decide on (i) which arm to play, (ii) whether to, and if so (iii) what and whom to communicate with. Agents in our model are decentralized, namely their actions only depend on their observed history in the past. #R##N#We develop a novel algorithm in which agents, whenever they choose, communicate only arm-ids and not samples, with another agent chosen uniformly and independently at random. The per-agent regret scaling achieved by our algorithm is $O \left( \frac{\lceil\frac{K}{n}\rceil+\log(n)}{\Delta} #R##N#\log(T) + \frac{\log^3(n) \log \log(n)}{\Delta^2} #R##N#\right)$. Furthermore, any agent in our algorithm communicates only a total of $\Theta(\log(T))$ times over a time interval of $T$. #R##N#We compare our results to two benchmarks - one where there is no communication among agents and one corresponding to complete interaction. We show both theoretically and empirically, that our algorithm experiences a significant reduction both in per-agent regret when compared to the case when agents do not collaborate and in communication complexity when compared to the full interaction setting which requires $T$ communication attempts by an agent over $T$ arm pulls.

Includes members:
  Member "collaboratively learning the best option on graphs using bounded local memory":
    also connected through other hyperedges:
      (none)
  Member "decentralized cooperative stochastic multi armed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the gossiping insert eliminate algorithm for multi agent bandits")
  Member "a distributed learning dynamics in social groups":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "collaboratively learning the best option on graphs using bounded local memory")
  Member "distributed cooperative decision making in multiarmed bandits frequentist and bayesian algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the gossiping insert eliminate algorithm for multi agent bandits")
  Member "collaborative learning of stochastic bandits over a social network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the gossiping insert eliminate algorithm for multi agent bandits")
  Member "delay and cooperation in nonstochastic bandits":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the gossiping insert eliminate algorithm for multi agent bandits")
  Member "human collective intelligence as distributed bayesian inference":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a distributed learning dynamics in social groups")
  Member "collaborative filtering bandits":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "estimation action reflection towards deep interaction between conversational and recommender systems")

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)