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: "contextual bandit learning with predictable rewards"
  Abstract: Contextual bandit learning is a reinforcement learning problem where the learner repeatedly receives a set of features (context), takes an action and receives a reward based on the action and context. We consider this problem under a realizability assumption: there exists a function in a (known) function class, always capable of predicting the expected reward, given the action and context. Under this assumption, we show three things. We present a new algorithm---Regressor Elimination--- with a regret similar to the agnostic setting (i.e. in the absence of realizability assumption). We prove a new lower bound showing no algorithm can achieve superior performance in the worst case even with the realizability assumption. However, we do show that for any set of policies (mapping contexts to actions), there is a distribution over rewards (given context) such that our new algorithm has constant regret unlike the previous approaches.

Connected through hyperedge HE-1 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles"):
  with members:
    - "efficient and robust algorithms for adversarial linear contextual bandits"
    - "introduction to online convex optimization"
    - "comments on the du kakade wang yang lower bounds"
    - "is a good representation sufficient for sample efficient reinforcement learning"
    - "model selection for contextual bandits"
    - "contextual bandits with surrogate losses margin bounds and efficient algorithms"
    - "new insights into bootstrapping for bandits"
    - "practical contextual bandits with regression oracles"

Connected through hyperedge HE-2 (induced by source paper "model selection for contextual bandits"):
  with members:
    - "a new algorithm for non stationary contextual bandits efficient optimal and parameter free"
    - "estimating learnability in the sublinear data regime"
    - "learning to optimize under non stationarity"
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "the externalities of exploration and how data diversity helps exploitation"
    - "practical contextual bandits with regression oracles"
    - "make the minority great again first order regret bound for contextual bandits"
    - "provably optimal algorithms for generalized linear contextual bandits"

Connected through hyperedge HE-3 (induced by source paper "adversarial bandits with knapsacks"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "introduction to online convex optimization"
    - "introduction to multi armed bandits"
    - "bandit convex optimization for scalable and dynamic iot management"
    - "acceleration through optimistic no regret dynamics"
    - "stochastic bandits robust to adversarial corruptions"
    - "more adaptive algorithms for adversarial bandits"
    - "unifying the stochastic and the adversarial bandits with knapsack"

Connected through hyperedge HE-4 (induced by source paper "contextual bandits with surrogate losses margin bounds and efficient algorithms"):
  with members:
    - "practical contextual bandits with regression oracles"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "improved regret bounds for oracle based adversarial contextual bandits"
    - "making contextual decisions with low technical debt"
    - "efficient algorithms for adversarial contextual learning"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "the price of bandit information in multiclass online classification"
    - "on the universality of online mirror descent"

Connected through hyperedge HE-5 (induced by source paper "practical contextual bandits with regression oracles"):
  with members:
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "active learning for cost sensitive classification"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "scikit learn machine learning in python"
    - "the offset tree for learning with partial labels"

Connected through hyperedge HE-6 (induced by source paper "making contextual decisions with low technical debt"):
  with members:
    - "clipper a low latency online prediction serving system"
    - "learning reductions that really work"
    - "ice enabling non experts to build models interactively for large scale lopsided problems"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "the missing piece in complex analytics low latency scalable model management and serving with velox"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"

Connected through hyperedge HE-7 (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"
    - "distributed online learning in social recommender systems"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "a comprehensive approach to universal piecewise nonlinear regression based on trees"
    - "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-8 (induced by source paper "a multiworld testing decision service"):
  with members:
    - "learning reductions that really work"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "the missing piece in complex analytics low latency scalable model management and serving with velox"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"
    - "a reliable effective terascale linear learning system"
    - "agnostic active learning without constraints"

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)