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: "modelling transition dynamics in mdps with rkhs embeddings"
  Abstract: We propose a new, nonparametric approach to learning and representing transition dynamics in Markov decision processes (MDPs), which can be combined easily with dynamic programming methods for policy optimisation and value estimation. This approach makes use of a recently developed representation of conditional distributions as \emph{embeddings} in a reproducing kernel Hilbert space (RKHS). Such representations bypass the need for estimating transition probabilities or densities, and apply to any domain on which kernels can be defined. This avoids the need to calculate intractable integrals, since expectations are represented as RKHS inner products whose computation has linear complexity in the number of points used to represent the embedding. We provide guarantees for the proposed applications in MDPs: in the context of a value iteration algorithm, we prove convergence to either the optimal policy, or to the closest projection of the optimal policy in our model class (an RKHS), under reasonable assumptions. In experiments, we investigate a learning task in a typical classical control setting (the under-actuated pendulum), and on a navigation problem where only images from a sensor are observed. For policy optimisation we compare with least-squares policy iteration where a Gaussian process is used for value function estimation. For value estimation we also compare to the NPDP method. Our approach achieves better performance in all experiments.

Connected through hyperedge HE-1 (induced by source paper "minimax optimal off policy evaluation with linear function approximation"):
  with members:
    - "asymptotically efficient off policy evaluation for tabular reinforcement learning"
    - "off policy policy gradient with state distribution correction"
    - "optimal off policy evaluation for reinforcement learning with marginalized importance sampling"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
    - "reinforcement leaning in feature space matrix bandit kernels and regret bound"
    - "doubly robust off policy value evaluation for reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "a measure theoretic approach to kernel conditional mean embeddings"):
  with members:
    - "causal inference via kernel deviance measures"
    - "operator valued kernels for learning from functional response data"
    - "hilbert space embeddings of pomdps"
    - "a kernel method for the two sample problem"

Connected through hyperedge HE-3 (induced by source paper "barrier certified adaptive reinforcement learning with applications to brushbot navigation"):
  with members:
    - "safe learning of quadrotor dynamics using barrier certificates"
    - "constrained policy optimization"
    - "the robotarium a remotely accessible swarm robotics research testbed"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "safe learning of regions of attraction for uncertain nonlinear systems with gaussian processes"
    - "safe policy search for lifelong reinforcement learning with sublinear regret"
    - "adaptive learning in cartesian product of reproducing kernel hilbert spaces"
    - "proximal reinforcement learning a new theory of sequential decision making in primal dual spaces"

Connected through hyperedge HE-4 (induced by source paper "nonparametric stochastic compositional gradient descent for q learning in continuous markov decision problems"):
  with members:
    - "learning from conditional distributions via dual kernel embeddings"
    - "continuous control with deep reinforcement learning"
    - "playing atari with deep reinforcement learning"
    - "when is there a representer theorem vector versus matrix regularizers"

Connected through hyperedge HE-5 (induced by source paper "organizing experience a deeper look at replay mechanisms for sample based planning in continuous state domains"):
  with members:
    - "efficient learning and planning with compressed predictive states"
    - "dyna style planning with linear function approximation and prioritized sweeping"

Connected through hyperedge HE-6 (induced by source paper "learning nash equilibrium for general sum markov games from batch data"):
  with members:
    - "continuous control with deep reinforcement learning"
    - "playing atari with deep reinforcement learning"
    - "value function approximation in noisy environments using locally smoothed regularized approximate linear programs"
    - "value function approximation in zero sum markov games"

Connected through hyperedge HE-7 (induced by source paper "doubly robust off policy value evaluation for reinforcement learning"):
  with members:
    - "data efficient off policy policy evaluation for reinforcement learning"
    - "recurrent reinforcement learning a hybrid approach"
    - "an emphatic approach to the problem of off policy temporal difference learning"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"

Connected through hyperedge HE-8 (induced by source paper "practical kernel based reinforcement learning"):
  with members:
    - "on the complexity of nonnegative matrix factorization"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

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)