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: "exploring compact reinforcement learning representations with linear regression"
  Abstract: This paper presents a new algorithm for online linear regression whose efficiency guarantees satisfy the requirements of the KWIK (Knows What It Knows) framework. The algorithm improves on the complexity bounds of the current state-of-the-art procedure in this setting. We explore several applications of this algorithm for learning compact reinforcement-learning representations. We show that KWIK linear regression can be used to learn the reward function of a factored MDP and the probabilities of action outcomes in Stochastic STRIPS and Object Oriented MDPs, none of which have been proven to be efficiently learnable in the RL setting before. We also combine KWIK linear regression with other KWIK learners to learn larger portions of these models, including experiments on learning factored MDP transition and reward functions together.

Connected through hyperedge HE-1 (induced by source paper "dora the explorer directed outreaching reinforcement action selection"):
  with members:
    - "vime variational information maximizing exploration"
    - "efficient bayes adaptive reinforcement learning using sample based search"
    - "reinforcement learning a survey"

Connected through hyperedge HE-2 (induced by source paper "a theoretical analysis of two stage recommendation for cold start collaborative filtering"):
  with members:
    - "budget constrained item cold start handling in collaborative filtering recommenders via optimal design"
    - "empirical analysis of predictive algorithms for collaborative filtering"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"

Connected through hyperedge HE-3 (induced by source paper "conquer confusion queried online bandit learning"):
  with members:
    - "online structured prediction via coactive learning"
    - "on multilabel classification and ranking with partial feedback"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "online learning with preference feedback"

Connected through hyperedge HE-4 (induced by source paper "the utility of abstaining in binary classification"):
  with members:
    - "active learning using smooth relative regret approximations with applications"
    - "agnostic active learning without constraints"
    - "importance weighted active learning"

Connected through hyperedge HE-5 (induced by source paper "differentially private linear algebra in the streaming model"):
  with members:
    - "circulant matrices and differential privacy"
    - "privacy via the johnson lindenstrauss transform"
    - "the noisy power method a meta algorithm with applications"
    - "calibrating data to sensitivity in private data analysis"
    - "differentially private data analysis of social networks via restricted sensitivity"
    - "the johnson lindenstrauss transform itself preserves differential privacy"
    - "beating randomized response on incoherent matrices"
    - "beyond worst case analysis in private singular vector computation"

Connected through hyperedge HE-6 (induced by source paper "near optimal brl using optimistic local transitions"):
  with members:
    - "variance based rewards for approximate bayesian reinforcement learning"
    - "a bayesian sampling approach to exploration in reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "learning is planning near bayes optimal reinforcement learning via monte carlo tree search"):
  with members:
    - "variance based rewards for approximate bayesian reinforcement learning"
    - "a bayesian sampling approach to exploration in reinforcement learning"

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)