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: "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"
  Abstract: Contextual bandit algorithms have become popular for online recommendation systems such as Digg, Yahoo! Buzz, and news recommendation in general. Offline evaluation of the effectiveness of new algorithms in these applications is critical for protecting online user experiences but very challenging due to their "partial-label" nature. Common practice is to create a simulator which simulates the online environment for the problem at hand and then run an algorithm against this simulator. However, creating simulator itself is often difficult and modeling bias is usually unavoidably introduced. In this paper, we introduce a replay methodology for contextual bandit algorithm evaluation. Different from simulator-based approaches, our method is completely data-driven and very easy to adapt to different applications. More importantly, our method can provide provably unbiased evaluations. Our empirical results on a large-scale news article recommendation dataset collected from Yahoo! Front Page conform well with our theoretical results. Furthermore, comparisons between our offline replay and online bucket evaluation of several contextual bandit algorithms show accuracy and effectiveness of our offline evaluation method.

Connected through hyperedge HE-1 (induced by source paper "self supervised contextual bandits in computer vision"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "exploiting unlabeled data in cnns by self supervised learning to rank"
    - "self supervised exploration via disagreement"
    - "boosting few shot visual learning with self supervision"
    - "semi supervised learning with scarce annotations"
    - "deep clustering for unsupervised learning of visual features"
    - "two stream self supervised learning for action recognition"
    - "large scale study of curiosity driven learning"

Connected through hyperedge HE-2 (induced by source paper "safe counterfactual reinforcement learning"):
  with members:
    - "intrinsically efficient stable and bounded off policy evaluation for reinforcement learning"
    - "off policy evaluation via off policy classification"
    - "importance sampling policy evaluation with an estimated behavior policy"
    - "efficient counterfactual learning from bandit feedback"
    - "representation balancing mdps for off policy policy evaluation"
    - "more robust doubly robust off policy evaluation"
    - "interpolated policy gradient merging on policy and off policy gradient estimation for deep reinforcement learning"
    - "data efficient off policy policy evaluation for reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "safe exploration for optimizing contextual bandits"):
  with members:
    - "to model or to intervene a comparison of counterfactual and online learning to rank from user interactions"
    - "bubblerank safe online learning to re rank via implicit click feedback"
    - "boltzmann exploration done right"
    - "balancing speed and quality in online learning to rank for information retrieval"
    - "unbiased learning to rank with biased feedback"
    - "risk aware algorithms for adversarial contextual bandits"
    - "off policy evaluation for slate recommendation"
    - "counterfactual risk minimization learning from logged bandit feedback"

Connected through hyperedge HE-4 (induced by source paper "asymptotically efficient off policy evaluation for tabular reinforcement learning"):
  with members:
    - "combining parametric and nonparametric models for off policy evaluation"
    - "double reinforcement learning for efficient off policy evaluation in markov decision processes"
    - "on the optimality of sparse model based planning for markov decision processes"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "representation balancing mdps for off policy policy evaluation"
    - "is q learning provably efficient"
    - "more robust doubly robust off policy evaluation"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "contextual bandit based personalized recommendation with time varying user interests"):
  with members:
    - "adalinucb opportunistic learning for contextual bandits"
    - "learning contextual bandits in a non stationary environment"
    - "a gang of bandits"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-6 (induced by source paper "better boosting with bandits for online learning"):
  with members:
    - "online isotonic regression"
    - "thompson sampling in switching environments with bayesian online change point detection"
    - "further optimal regret bounds for thompson sampling"
    - "obtaining calibrated probabilities from boosting"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "multi armed bandits in metric spaces"

Connected through hyperedge HE-7 (induced by source paper "concentration bounds for temporal difference learning with linear function approximation the case of batch data and uniform sampling"):
  with members:
    - "a finite time analysis of temporal difference learning with linear function approximation"
    - "finite sample analyses for td 0 with function approximation"
    - "on convergence of emphatic temporal difference learning"
    - "fast lstd using stochastic approximation finite time analysis and application to traffic control"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-8 (induced by source paper "conversational contextual bandit algorithm and application"):
  with members:
    - "active learning in recommendation systems with multi level user preferences"
    - "collaborative filtering bandits"
    - "a gang of bandits"
    - "hierarchical exploration for accelerating contextual bandits"
    - "thompson sampling for contextual bandits with linear payoffs"

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)