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: "discrete sequential prediction of continuous actions for deep rl"
  Abstract: It has long been assumed that high dimensional continuous control problems cannot be solved effectively by discretizing individual dimensions of the action space due to the exponentially large number of bins over which policies would have to be learned. In this paper, we draw inspiration from the recent success of sequence-to-sequence models for structured prediction problems to develop policies over discretized spaces. Central to this method is the realization that complex functions over high dimensional spaces can be modeled by neural networks that predict one dimension at a time. Specifically, we show how Q-values and policies over continuous spaces can be modeled using a next step prediction model over discretized dimensions. With this parameterization, it is possible to both leverage the compositional structure of action spaces during learning, as well as compute maxima over action spaces (approximately). On a simple example task we demonstrate empirically that our method can perform global search, which effectively gets around the local optimization issues that plague DDPG. We apply the technique to off-policy (Q-learning) methods and show that our method can achieve the state-of-the-art for off-policy methods on several continuous control tasks.

Connected through hyperedge HE-1 (induced by source paper "particle based adaptive discretization for continuous control using deep reinforcement learning"):
  with members:
    - "discretizing continuous action space for on policy optimization"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "sim to real learning agile locomotion for quadruped robots"
    - "sfv reinforcement learning of physical skills from videos"
    - "soft actor critic algorithms and applications"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "learning to walk via deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "q learning in enormous action spaces via amortized approximate maximization"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "deepmind control suite"
    - "learning latent dynamics for planning from pixels"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "distributed prioritized experience replay"
    - "sampled policy gradient for learning to play the game agar io"
    - "entropic policy composition with generalized policy improvement and divergence correction"

Connected through hyperedge HE-3 (induced by source paper "cost efficient hierarchical knowledge extraction with deep reinforcement learning"):
  with members:
    - "effective medical test suggestions using deep reinforcement learning"
    - "classification with costly features as a sequential decision making problem"
    - "discretizing continuous action space for on policy optimization"
    - "graph neural networks a review of methods and applications"
    - "efficient entropy for policy gradient with multidimensional action space"
    - "approximation capability of neural networks on spaces of probability measures and tree structured domains"
    - "discriminative models for multi instance problems with tree structure"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "language as an abstraction for hierarchical deep reinforcement learning"):
  with members:
    - "skew fit state covering self supervised reinforcement learning"
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "a survey of reinforcement learning informed by natural language"
    - "hierarchical reinforcement learning with hindsight"
    - "temporal difference models model free deep rl for model based control"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "visual reinforcement learning with imagined goals"

Connected through hyperedge HE-5 (induced by source paper "recsim a configurable simulation platform for recommender systems"):
  with members:
    - "reinforcement learning when all actions are not always available"
    - "advantage amplification in slowly evolving latent state environments"
    - "challenges of real world reinforcement learning"
    - "toward simulating environments in reinforcement learning based recommendations"
    - "dopamine a research framework for deep reinforcement learning"
    - "deep reinforcement learning for page wise recommendations"
    - "learning a deep listwise context model for ranking refinement"
    - "natural environment benchmarks for reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "discrete and continuous action representation for practical rl in video games"):
  with members:
    - "distilling policy distillation"
    - "soft actor critic for discrete action settings"
    - "discretizing continuous action space for on policy optimization"
    - "improving exploration in soft actor critic with normalizing flows policies"
    - "leveraging exploration in off policy algorithms via normalizing flows"
    - "managing engineering systems with large state and action spaces through deep reinforcement learning"
    - "hierarchical approaches for reinforcement learning in parameterized action space"
    - "soft actor critic algorithms and applications"

Connected through hyperedge HE-7 (induced by source paper "discretizing continuous action space for on policy optimization"):
  with members:
    - "simple random search provides a competitive approach to reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "implicit policy for reinforcement learning"
    - "fast approximate natural gradient descent in a kronecker factored eigenbasis"
    - "latent space policies for hierarchical reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"

Connected through hyperedge HE-8 (induced by source paper "deep reinforcement learning with explicitly represented knowledge and variable state and action spaces"):
  with members:
    - "effective medical test suggestions using deep reinforcement learning"
    - "classification with costly features as a sequential decision making problem"
    - "discretizing continuous action space for on policy optimization"
    - "graph neural networks a review of methods and applications"
    - "relational deep reinforcement learning"
    - "efficient entropy for policy gradient with multidimensional action space"
    - "approximation capability of neural networks on spaces of probability measures and tree structured domains"
    - "using neural network formalism to solve multiple instance problems"

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)