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: "online stochastic linear optimization under one bit feedback"
  Abstract: In this paper, we study a special bandit setting of online stochastic linear optimization, where only one-bit of information is revealed to the learner at each round. This problem has found many applications including online advertisement and online recommendation. We assume the binary feedback is a random variable generated from the logit model, and aim to minimize the regret defined by the unknown linear function. Although the existing method for generalized linear bandit can be applied to our problem, the high computational cost makes it impractical for real-world problems. To address this challenge, we develop an efficient online learning algorithm by exploiting particular structures of the observation model. Specifically, we adopt online Newton step to estimate the unknown parameter and derive a tight confidence region based on the exponential concavity of the logistic loss. Our analysis shows that the proposed algorithm achieves a regret bound of $O(d\sqrt{T})$, which matches the optimal result of stochastic linear bandits.

Connected through hyperedge HE-1 (induced by source paper "old dog learns new tricks randomized ucb for bandit problems"):
  with members:
    - "empirical bayes regret minimization"
    - "perturbed history exploration in stochastic linear bandits"
    - "randomized exploration in generalized linear bandits"
    - "garbage in reward out bootstrapping exploration in multi armed bandits"
    - "new insights into bootstrapping for bandits"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "a practical method for solving contextual bandit problems using decision trees"
    - "thompson sampling with the online bootstrap"

Connected through hyperedge HE-2 (induced by source paper "perturbed history exploration in stochastic multi armed bandits"):
  with members:
    - "perturbed history exploration in stochastic linear bandits"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "a tutorial on thompson sampling"
    - "customized nonlinear bandits for online response selection in neural conversation models"
    - "dcm bandits learning to rank with multiple clicks"
    - "bbq networks efficient exploration in deep reinforcement learning for task oriented dialogue systems"
    - "cascading bandits learning to rank in the cascade model"
    - "further optimal regret bounds for thompson sampling"

Connected through hyperedge HE-3 (induced by source paper "contextual markov decision processes using generalized linear models"):
  with members:
    - "policy certificates towards accountable reinforcement learning"
    - "logistic regression the importance of being improper"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "online learning in mdps with side information"

Connected through hyperedge HE-4 (induced by source paper "garbage in reward out bootstrapping exploration in multi armed bandits"):
  with members:
    - "perturbed history exploration in stochastic multi armed bandits"
    - "perturbed history exploration in stochastic linear bandits"
    - "new insights into bootstrapping for bandits"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "a practical method for solving contextual bandit problems using decision trees"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "thompson sampling with the online bootstrap"
    - "api design for machine learning software experiences from the scikit learn project"

Connected through hyperedge HE-5 (induced by source paper "new insights into bootstrapping for bandits"):
  with members:
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "a practical method for solving contextual bandit problems using decision trees"
    - "deep exploration via bootstrapped dqn"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "thompson sampling with the online bootstrap"
    - "api design for machine learning software experiences from the scikit learn project"
    - "further optimal regret bounds for thompson sampling"
    - "thompson sampling for contextual bandits with linear payoffs"

Connected through hyperedge HE-6 (induced by source paper "online optimal task offloading with one bit feedback"):
  with members:
    - "bandit convex optimization for scalable and dynamic iot management"
    - "learn and pick right nodes to offload"
    - "a survey on mobile edge computing the communication perspective"
    - "energy efficient resource allocation for mobile edge computation offloading"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

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)