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: "off policy evaluation for slate recommendation"
  Abstract: This paper studies the evaluation of policies that recommend an ordered set of items (e.g., a ranking) based on some context---a common scenario in web search, ads, and recommendation. We build on techniques from combinatorial bandits to introduce a new practical estimator that uses logged data to estimate a policy's performance. A thorough empirical evaluation on real-world data reveals that our estimator is accurate in a variety of settings, including as a subroutine in a learning-to-rank task, where it achieves competitive performance. We derive conditions under which our estimator is unbiased---these conditions are weaker than prior heuristics for slate evaluation---and experimentally demonstrate a smaller bias than parametric approaches, even when these conditions are violated. Finally, our theory and experiments also show exponential savings in the amount of required data compared with general unbiased estimators.

Connected through hyperedge HE-1 (induced by source paper "delay adaptive learning in generalized linear contextual bandits"):
  with members:
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "thompson sampling for the mnl bandit"
    - "delay and cooperation in nonstochastic bandits"
    - "asymptotic convergence in online learning with unbounded delays"
    - "an efficient algorithm for contextual bandits with knapsacks and an extension to concave objectives"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "an information theoretic analysis of thompson sampling"
    - "learning to optimize via posterior sampling"

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"
    - "counterfactual risk minimization learning from logged bandit feedback"
    - "introducing letor 4 0 datasets"

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 "adaptive estimator selection for off policy evaluation"):
  with members:
    - "doubly robust off policy evaluation with shrinkage"
    - "empirical study of off policy policy evaluation for reinforcement learning"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "more robust doubly robust off policy evaluation"
    - "data efficient off policy policy evaluation for reinforcement learning"
    - "safe and efficient off policy reinforcement learning"
    - "doubly robust off policy value evaluation for reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "empirical study of off policy policy evaluation for reinforcement learning"):
  with members:
    - "gradientdice rethinking generalized offline estimation of stationary values"
    - "counterfactual off policy evaluation with gumbel max structural causal models"
    - "double reinforcement learning for efficient off policy evaluation in markov decision processes"
    - "off policy evaluation in partially observable environments"
    - "minimax weight and q function learning for off policy evaluation"
    - "off policy policy gradient with state distribution correction"
    - "batch policy learning under constraints"
    - "information theoretic considerations in batch reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "dualdice behavior agnostic estimation of discounted stationary distribution corrections"):
  with members:
    - "off policy policy gradient with state distribution correction"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "learning dexterous in hand manipulation"
    - "neural approaches to conversational ai"
    - "more robust doubly robust off policy evaluation"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "stochastic variance reduction methods for policy evaluation"

Connected through hyperedge HE-8 (induced by source paper "causal modeling for fairness in dynamical systems"):
  with members:
    - "counterfactual off policy evaluation with gumbel max structural causal models"
    - "understanding agent incentives using causal influence diagrams part i single action settings"
    - "the disparate effects of strategic manipulation"
    - "downstream effects of affirmative action"
    - "a short term intervention for long term fairness in the labor market"
    - "the social cost of strategic classification"
    - "woulda coulda shoulda counterfactually guided policy search"
    - "fairness through causal awareness learning latent variable models for biased data"

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)