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 via cooperative contextual bandits"
  Abstract: In this paper we propose a novel framework for decentralized, online learning by many learners. At each moment of time, an instance characterized by a certain context may arrive to each learner; based on the context, the learner can select one of its own actions (which gives a reward and provides information) or request assistance from another learner. In the latter case, the requester pays a cost and receives the reward but the provider learns the information. In our framework, learners are modeled as cooperative contextual bandits. Each learner seeks to maximize the expected reward from its arrivals, which involves trading off the reward received from its own actions, the information learned from its own actions, the reward received from the actions requested of others and the cost paid for these actions - taking into account what it has learned about the value of assistance from each other learner. We develop distributed online learning algorithms and provide analytic bounds to compare the efficiency of these with algorithms with the complete knowledge (oracle) benchmark (in which the expected reward of every action in every context is known by every learner). Our estimates show that regret - the loss incurred by the algorithm - is sublinear in time. Our theoretical framework can be used in many practical applications including Big Data mining, event detection in surveillance sensor networks and distributed online recommendation systems.

Connected through hyperedge HE-1 (induced by source paper "action manipulation attacks against stochastic bandits attacks and defense"):
  with members:
    - "robust stochastic bandit algorithms under probabilistic unbounded adversarial attack"
    - "data poisoning attacks on stochastic bandits"
    - "the intrinsic robustness of stochastic bandits to strategic manipulation"
    - "stochastic bandits robust to adversarial corruptions"
    - "data poisoning attacks in contextual bandits"
    - "is feature selection secure against training data poisoning"
    - "adversarial attacks on neural network policies"
    - "tactics of adversarial attack on deep reinforcement learning agents"

Connected through hyperedge HE-2 (induced by source paper "budget constrained edge service provisioning with demand estimation via bandit learning"):
  with members:
    - "joint service caching and task offloading for mobile edge computing in dense networks"
    - "spatio temporal edge service placement a bandit learning approach"
    - "a survey on mobile edge computing the communication perspective"
    - "computation peer offloading for energy constrained mobile edge computing in small cell networks"
    - "socially trusted collaborative edge computing in ultra dense networks"
    - "context aware proactive content caching with service differentiation in wireless networks"
    - "adaptive ensemble learning with confidence bounds"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-3 (induced by source paper "minimax optimal algorithms for adversarial bandit problem with multiple plays"):
  with members:
    - "explore no more improved high probability regret bounds for non stochastic bandits"
    - "importance weighting without importance weights an efficient algorithm for combinatorial semi bandits"
    - "feedback solution to optimal switching problems with switching cost"
    - "on optimality of myopic policy for restless multi armed bandit problem an axiomatic approach"
    - "regret in online combinatorial optimization"
    - "contextual bandit algorithms with supervised learning guarantees"
    - "the on line shortest path problem under partial monitoring"

Connected through hyperedge HE-4 (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"
    - "distributed online learning in social recommender systems"
    - "taming the monster a fast and simple algorithm for 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-5 (induced by source paper "spatio temporal edge service placement a bandit learning approach"):
  with members:
    - "joint service caching and task offloading for mobile edge computing in dense networks"
    - "online learning for offloading and autoscaling in energy harvesting mobile edge computing"
    - "a survey on mobile edge computing the communication perspective"
    - "socially trusted collaborative edge computing in ultra dense networks"
    - "context aware proactive content caching with service differentiation in wireless networks"
    - "collaborative service caching for edge computing in dense small cell networks"
    - "delay optimal computation task scheduling for mobile edge computing systems"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-6 (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"
    - "distributed online learning in social recommender systems"
    - "online clustering of bandits"
    - "resourceful 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-7 (induced by source paper "global bandits"):
  with members:
    - "bounded regret for finite armed structured bandits"
    - "an information theoretic analysis of thompson sampling"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "from bandits to experts on the value of side observations"
    - "an optimal algorithm for linear bandits"
    - "a contextual bandit approach to personalized news article recommendation"
    - "contextual bandits with similarity information"

Connected through hyperedge HE-8 (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"
    - "distributed online learning in social recommender systems"
    - "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"

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)