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: "equivalence between wasserstein and value aware model based reinforcement learning"
  Abstract: Learning a generative model is a key component of model-based reinforcement learning. Though learning a good model in the tabular setting is a simple task, learning a useful model in the approximate setting is challenging. Recently Farahmand et al. (2017) proposed a value-aware (VAML) objective that captures the structure of value function during model learning. Using tools from Lipschitz continuity, we show that minimizing the VAML objective is in fact equivalent to minimizing the Wasserstein metric.

Connected through hyperedge HE-1 (induced by source paper "policy aware model learning for policy gradient methods"):
  with members:
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "adaptive trust region policy optimization global convergence and faster rates for regularized mdps"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "information theoretic considerations in batch reinforcement learning"
    - "gradient aware model based policy search"
    - "global optimality guarantees for policy gradient methods"
    - "model ensemble trust region policy optimization"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"

Connected through hyperedge HE-2 (induced by source paper "combating the compounding error problem with a multi step model"):
  with members:
    - "deepmdp learning continuous latent space models for representation learning"
    - "model ensemble trust region policy optimization"
    - "model based active exploration"
    - "lipschitz continuity in model based reinforcement learning"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"
    - "model based rl in contextual decision processes pac bounds and exponential improvements over model free approaches"
    - "multi step reinforcement learning a unifying algorithm"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"

Connected through hyperedge HE-3 (induced by source paper "formal verification of higher order probabilistic programs reasoning about approximation convergence bayesian inference and optimization"):
  with members:
    - "relational reasoning for markov chains in a probabilistic guarded lambda calculus"
    - "measurable cones and stable measurable functions a model for probabilistic higher order programming"
    - "mixed powerdomains for probability and nondeterminism"
    - "a convenient category for higher order probability theory"
    - "denotational validation of higher order bayesian inference"
    - "a theory of slicing for probabilistic control flow graphs"
    - "a relational logic for higher order programs"
    - "a program logic for union bounds"

Connected through hyperedge HE-4 (induced by source paper "gradient aware model based policy search"):
  with members:
    - "model based reinforcement learning for atari"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"
    - "model based value estimation for efficient model free reinforcement learning"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "recurrent world models facilitate policy evolution"
    - "value prediction network"
    - "task based end to end model learning in stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "lipschitz continuity in model based reinforcement learning"):
  with members:
    - "lipschitz properties for deep convolutional networks"
    - "a distributional perspective on reinforcement learning"
    - "bridging the gap between value and policy based reinforcement learning"
    - "a unified view of entropy regularized markov decision processes"
    - "continuous deep q learning with model based acceleration"
    - "self correcting models for model based reinforcement learning"
    - "an alternative softmax operator for reinforcement learning"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "distributional multivariate policy evaluation and exploration with the bellman gan"):
  with members:
    - "exploration by distributional reinforcement learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "a distributional perspective on reinforcement learning"
    - "improved training of wasserstein gans"
    - "distributional reinforcement learning with quantile regression"
    - "curiosity driven exploration by self supervised prediction"
    - "vime variational information maximizing exploration"
    - "learning to act by predicting the future"

Connected through hyperedge HE-7 (induced by source paper "towards a simple approach to multi step model based reinforcement learning"):
  with members:
    - "lipschitz continuity in model based reinforcement learning"
    - "model based value estimation for efficient model free reinforcement learning"
    - "value prediction network"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"
    - "the predictron end to end learning and planning"
    - "self correcting models for model based reinforcement learning"
    - "action conditional video prediction using deep networks in atari games"

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)