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: "a gang of bandits"
  Abstract: Multi-armed bandit problems are receiving a great deal of attention because they adequately formalize the exploration-exploitation trade-offs arising in several industrially relevant applications, such as online advertisement and, more generally, recommendation systems. In many cases, however, these applications have a strong social component, whose integration in the bandit algorithm could lead to a dramatic performance increase. For instance, we may want to serve content to a group of users by taking advantage of an underlying network of social relationships among them. In this paper, we introduce novel algorithmic approaches to the solution of such networked bandit problems. More specifically, we design and analyze a global strategy which allocates a bandit algorithm to each network node (user) and allows it to "share" signals (contexts and payoffs) with the neghboring nodes. We then derive two more scalable variants of this strategy based on different ways of clustering the graph nodes. We experimentally compare the algorithm and its variants to state-of-the-art methods for contextual bandits that do not use the relational information. Our experiments, carried out on synthetic and real-world datasets, show a marked increase in prediction performance obtained by exploiting the network structure.

Connected through hyperedge HE-1 (induced by source paper "rapidly personalizing mobile health treatment policies with limited data"):
  with members:
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"
    - "encouraging physical activity in patients with diabetes intervention using a reinforcement learning system"
    - "on kernelized multi armed bandits"
    - "context aware bandits"
    - "sparse gaussian processes for multi task learning"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "portfolio allocation for bayesian optimization"

Connected through hyperedge HE-2 (induced by source paper "contextual bandit based personalized recommendation with time varying user interests"):
  with members:
    - "adalinucb opportunistic learning for contextual bandits"
    - "learning contextual bandits in a non stationary environment"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-3 (induced by source paper "conversational contextual bandit algorithm and application"):
  with members:
    - "active learning in recommendation systems with multi level user preferences"
    - "collaborative filtering bandits"
    - "hierarchical exploration for accelerating contextual bandits"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"

Connected through hyperedge HE-4 (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-5 (induced by source paper "bilinear bandits with low rank structure"):
  with members:
    - "factored bandits"
    - "horde of bandits using gaussian markov random fields"
    - "bernoulli rank 1 bandits for click feedback"
    - "stochastic low rank bandits"
    - "stochastic rank 1 bandits"
    - "contextual bandits with latent confounders an nmf approach"
    - "on context dependent clustering of bandits"
    - "low rank bandits with latent mixtures"

Connected through hyperedge HE-6 (induced by source paper "laplacian regularized graph bandits algorithms and theoretical analysis"):
  with members:
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "alternating linear bandits for online matrix factorization recommendation"
    - "almost optimal algorithms for linear stochastic bandits with heavy tailed payoffs"
    - "graph based recommendation system"
    - "horde of bandits using gaussian markov random fields"
    - "distributed clustering of linear bandits in peer to peer networks"
    - "collaborative filtering bandits"
    - "learning laplacian matrix in smooth graph signal representations"

Connected through hyperedge HE-7 (induced by source paper "toward building conversational recommender systems a contextual bandit approach"):
  with members:
    - "conversational recommender system"
    - "active learning in recommendation systems with multi level user preferences"
    - "hierarchical exploration for accelerating contextual bandits"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"
    - "multi armed bandits in metric spaces"

Connected through hyperedge HE-8 (induced by source paper "learning contextual bandits in a non stationary environment"):
  with members:
    - "a change detection based framework for piecewise stationary multi armed bandit problem"
    - "efficient contextual bandits in non stationary worlds"
    - "misspecified linear bandits"
    - "on context dependent clustering of bandits"
    - "collaborative filtering bandits"
    - "online clustering of bandits"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"
    - "a contextual bandit approach to personalized news article recommendation"

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.LG(Machine Learning)