human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "social learning in multi agent multi armed bandits"):
  with members:
    - "collaboratively learning the best option on graphs using bounded local memory"
    - "decentralized cooperative stochastic multi armed bandits"
    - "a distributed learning dynamics in social groups"
    - "distributed cooperative decision making in multiarmed bandits frequentist and bayesian algorithms"
    - "collaborative learning of stochastic bandits over a social network"
    - "delay and cooperation in nonstochastic bandits"
    - "human collective intelligence as distributed bayesian inference"
    - "collaborative filtering bandits"

Connected through hyperedge HE-2 (induced by source paper "multi objective contextual multi armed bandit with a dominant objective"):
  with members:
    - "personalized course sequence recommendations"
    - "releaf an algorithm for learning and exploiting relevance"
    - "adaptive ensemble learning with confidence bounds"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "distributed online learning via cooperative contextual bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "efficient optimal learning for contextual bandits"
    - "solving the apparent diversity accuracy dilemma of recommender systems"

Connected through hyperedge HE-3 (induced by source paper "context aware hierarchical online learning for performance maximization in mobile crowdsourcing"):
  with members:
    - "context aware proactive content caching with service differentiation in wireless networks"
    - "dynamic assessments matching and allocation of tasks"
    - "online clustering of bandits"
    - "resourceful contextual bandits"
    - "distributed online learning via cooperative contextual bandits"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "fast reinforcement learning for energy efficient wireless communication"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-4 (induced by source paper "context aware proactive content caching with service differentiation in wireless networks"):
  with members:
    - "a non stochastic learning approach to energy efficient mobility management"
    - "multi armed bandits with application to 5g small cells"
    - "big data meets telcos a proactive caching perspective"
    - "learning based optimization of cache content in a small cell base station"
    - "content level selective offloading in heterogeneous networks multi armed bandit optimization and regret bounds"
    - "cache enabled small cell networks modeling and tradeoffs"
    - "living on the edge the role of proactive caching in 5g wireless networks"
    - "content aware user clustering and caching in wireless small cell networks"

Connected through hyperedge HE-5 (induced by source paper "an asymptotically optimal contextual bandit algorithm using hierarchical structures"):
  with members:
    - "randomized prediction games for adversarial machine learning"
    - "efficient algorithms for adversarial contextual learning"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "a comprehensive approach to universal piecewise nonlinear regression based on trees"
    - "contextual bandit learning with predictable rewards"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"
    - "efficient optimal learning for contextual bandits"

Connected through hyperedge HE-6 (induced by source paper "delay and cooperation in nonstochastic bandits"):
  with members:
    - "explore no more improved high probability regret bounds for non stochastic bandits"
    - "multi player bandits a musical chairs approach"
    - "parallel correlation clustering on big graphs"
    - "on distributed cooperative decision making in multiarmed bandits"
    - "nonstochastic multi armed bandits with graph structured feedback"
    - "online learning under delayed feedback"
    - "distributed online learning via cooperative contextual bandits"
    - "efficient optimal learning for contextual bandits"

Connected through hyperedge HE-7 (induced by source paper "differentially private online learning for cloud based video recommendation with multimedia big data in social networks"):
  with members:
    - "distributed online big data classification using context information"
    - "calibrating data to sensitivity in private data analysis"
    - "personalized social recommendations accurate or private"
    - "a contextual bandit approach to personalized news article recommendation"
    - "contextual bandits with similarity information"
    - "how to break anonymity of the netflix prize dataset"

Connected through hyperedge HE-8 (induced by source paper "forecasting popularity of videos using social media"):
  with members:
    - "popularity prediction in microblogging network a case study on sina weibo"
    - "efficient optimal learning for contextual bandits"
    - "using a model of social dynamics to predict popularity of news"
    - "contextual bandits with similarity information"
    - "predicting the popularity of online content"

Question: Please tell me which class the center node 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)