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: "batch policy learning under constraints"
  Abstract: When learning policies for real-world domains, two important questions arise: (i) how to efficiently use pre-collected off-policy, non-optimal behavior data; and (ii) how to mediate among different competing objectives and constraints. We thus study the problem of batch policy learning under multiple constraints, and offer a systematic solution. We first propose a flexible meta-algorithm that admits any batch reinforcement learning and online learning procedure as subroutines. We then present a specific algorithmic instantiation and provide performance guarantees for the main objective and all constraints. To certify constraint satisfaction, we propose a new and simple method for off-policy policy evaluation (OPE) and derive PAC-style bounds. Our algorithm achieves strong empirical results in different domains, including in a challenging problem of simulated car driving subject to multiple constraints such as lane keeping and smooth driving. We also show experimentally that our OPE method outperforms other popular OPE techniques on a standalone basis, especially in a high-dimensional setting.

Connected through hyperedge HE-1 (induced by source paper "upper confidence primal dual optimization stochastically constrained markov decision processes with adversarial losses and unknown transitions"):
  with members:
    - "constrained upper confidence reinforcement learning"
    - "provably efficient safe exploration via primal dual policy optimization"
    - "exploration enhanced politex"
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "model free reinforcement learning in infinite horizon average reward markov decision processes"
    - "online convex optimization in adversarial markov decision processes"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "introduction to online convex optimization"

Connected through hyperedge HE-2 (induced by source paper "regret bounds for batched bandits"):
  with members:
    - "adaptivity in adaptive submodularity"
    - "introduction to multi armed bandits"
    - "unconstrained submodular maximization with constant adaptive complexity"
    - "stochastic submodular cover with limited adaptivity"
    - "delay and cooperation in nonstochastic bandits"
    - "batched gaussian process bandit optimization via determinantal point processes"
    - "online learning with switching costs and other adaptive adversaries"
    - "parallel gaussian process optimization with upper confidence bound and pure exploration"

Connected through hyperedge HE-3 (induced by source paper "interpretable off policy evaluation in reinforcement learning by highlighting influential transitions"):
  with members:
    - "combining parametric and nonparametric models for off policy evaluation"
    - "evaluating reinforcement learning algorithms in observational health settings"
    - "policy certificates towards accountable reinforcement learning"
    - "bootstrapping with models confidence intervals for off policy evaluation"
    - "doubly robust off policy value evaluation for reinforcement learning"
    - "adaptively learning the crowd kernel"

Connected through hyperedge HE-4 (induced by source paper "imitation projected programmatic reinforcement learning"):
  with members:
    - "control regularization for reduced variance reinforcement learning"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "houdini lifelong learning as program synthesis"
    - "safe exploration in continuous action spaces"
    - "predictor corrector policy optimization"
    - "programmatically interpretable reinforcement learning"
    - "accelerating imitation learning with predictive models"
    - "verifiable reinforcement learning via policy extraction"

Connected through hyperedge HE-5 (induced by source paper "provable representation learning for imitation learning via bi level optimization"):
  with members:
    - "adaptive gradient based meta learning methods"
    - "learning to learn stochastic gradient descent with biased regularization"
    - "online meta learning"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "meta learning with implicit gradients"
    - "imitating latent policies from observation"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "task embedded control networks for few shot imitation 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"
    - "information theoretic considerations in batch reinforcement learning"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"

Connected through hyperedge HE-7 (induced by source paper "control regularization for reduced variance reinforcement learning"):
  with members:
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "a lyapunov based approach to safe reinforcement learning"
    - "residual reinforcement learning for robot control"
    - "programmatically interpretable reinforcement learning"
    - "measuring and regularizing networks in function space"
    - "deep reinforcement learning that matters"
    - "deep reinforcement learning a brief survey"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"

Connected through hyperedge HE-8 (induced by source paper "off policy evaluation in partially observable environments"):
  with members:
    - "counterfactual off policy evaluation with gumbel max structural causal models"
    - "double reinforcement learning for efficient off policy evaluation in markov decision processes"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "evaluating reinforcement learning algorithms in observational health settings"
    - "deconfounding reinforcement learning in observational settings"
    - "contextual bandits with latent confounders an nmf approach"
    - "linguistically regularized lstms for sentiment classification"

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)