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 "distributed online learning in social recommender systems"):
  Abstract: In this paper, we consider decentralized sequential decision making in distributed online recommender systems, where items are recommended to users based on their search query as well as their specific background including history of bought items, gender and age, all of which comprise the context information of the user. In contrast to centralized recommender systems, in which there is a single centralized seller who has access to the complete inventory of items as well as the complete record of sales and user information, in decentralized recommender systems each seller/learner only has access to the inventory of items and user information for its own products and not the products and user information of other sellers, but can get commission if it sells an item of another seller. Therefore, the sellers must distributedly find out for an incoming user which items to recommend (from the set of own items or items of another seller), in order to maximize the revenue from own sales and commissions. We formulate this problem as a cooperative contextual bandit problem, analytically bound the performance of the sellers compared to the best recommendation strategy given the complete realization of user arrivals and the inventory of items, as well as the context-dependent purchase probabilities of each item, and verify our results via numerical examples on a distributed data set adapted based on Amazon data. We evaluate the dependence of the performance of a seller on the inventory of items the seller has, the number of connections it has with the other sellers, and the commissions which the seller gets by selling items of other sellers to its users.

Includes members:
  Member "decentralized online big data classification a bandit framework":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed online learning via cooperative contextual bandits")
  Member "distributed online learning via cooperative contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "action manipulation attacks against stochastic bandits attacks and defense")
  Member "distributed online big data classification using context information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "differentially private online learning for cloud based video recommendation with multimedia big data in social networks")
  Member "linear bandits in high dimension and recommendation systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "contextual bandits with random projection")
  Member "an mdp based recommender system":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "recsim a configurable simulation platform for recommender systems")
  Member "efficient optimal learning for contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "a contextual bandit approach to personalized news article recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "contextual bandits with similarity information":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")

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.SI(Social and Information Networks)