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: "practical contextual bandits with regression oracles"
  Abstract: A major challenge in contextual bandits is to design general-purpose algorithms that are both practically useful and theoretically well-founded. We present a new technique that has the empirical and computational advantages of realizability-based approaches combined with the flexibility of agnostic methods. Our algorithms leverage the availability of a regression oracle for the value-function class, a more realistic and reasonable oracle than the classification oracles over policies typically assumed by agnostic methods. Our approach generalizes both UCB and LinUCB to far more expressive possible model classes and achieves low regret under certain distributional assumptions. In an extensive empirical evaluation, compared to both realizability-based and agnostic baselines, we find that our approach typically gives comparable or superior results.

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"
    - "make the minority great again first order regret bound for contextual bandits"

Connected through hyperedge HE-2 (induced by source paper "adaptive robot assisted feeding an online learning framework for acquiring previously unseen food items"):
  with members:
    - "robot assisted feeding generalizing skewering strategies across food items on a realistic plate"
    - "towards robotic feeding role of haptics in fork based food manipulation"
    - "focal loss for dense object detection"
    - "bandit based model selection for deformable object manipulation"
    - "towards assistive feeding with a general purpose mobile manipulator"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "exploration in interactive personalized music recommendation a reinforcement learning approach"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-3 (induced by source paper "generalized policy elimination an efficient algorithm for nonparametric contextual bandits"):
  with members:
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "efficient optimal learning for contextual bandits"

Connected through hyperedge HE-4 (induced by source paper "explicit explore exploit algorithms in continuous state spaces"):
  with members:
    - "provably efficient rl with rich observations via latent state decoding"
    - "self supervised exploration via disagreement"
    - "model predictive policy learning with uncertainty regularization for driving in dense traffic"
    - "exploration by random network distillation"
    - "learning latent dynamics for planning from pixels"
    - "model based active exploration"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"

Connected through hyperedge HE-5 (induced by source paper "corruption robust exploration in episodic reinforcement learning"):
  with members:
    - "stochastic linear optimization with adversarial corruption"
    - "provably efficient reinforcement learning with linear function approximation"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "online convex optimization in adversarial markov decision processes"
    - "beating stochastic and adversarial semi bandits optimally and simultaneously"
    - "better algorithms for stochastic bandits with adversarial corruptions"
    - "non asymptotic gap dependent regret bounds for tabular mdps"
    - "recent advances in algorithmic high dimensional robust statistics"

Connected through hyperedge HE-6 (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"
    - "make the minority great again first order regret bound for contextual bandits"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "small loss bounds for online learning with partial information"

Connected through hyperedge HE-7 (induced by source paper "boosting image recognition with non differentiable constraints"):
  with members:
    - "banditsum extractive summarization as a contextual bandit"
    - "coco text dataset and benchmark for text detection and recognition in natural images"
    - "deep residual learning for image recognition"
    - "trust region policy optimization"
    - "sequence level training with recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "multi digit number recognition from street view imagery using deep convolutional neural networks"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "learning from failures in robot assisted feeding using online learning to develop manipulation strategies for bite acquisition"):
  with members:
    - "robot assisted feeding generalizing skewering strategies across food items on a realistic plate"
    - "towards robotic feeding role of haptics in fork based food manipulation"
    - "focal loss for dense object detection"
    - "bandit based model selection for deformable object manipulation"
    - "towards assistive feeding with a general purpose mobile manipulator"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "exploration in interactive personalized music recommendation a reinforcement learning approach"
    - "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)