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: "action branching architectures for deep reinforcement learning"
  Abstract: Discrete-action algorithms have been central to numerous recent successes of deep reinforcement learning. However, applying these algorithms to high-dimensional action tasks requires tackling the combinatorial increase of the number of possible actions with the number of action dimensions. This problem is further exacerbated for continuous-action tasks that require fine control of actions via discretization. In this paper, we propose a novel neural architecture featuring a shared decision module followed by several network branches, one for each action dimension. This approach achieves a linear increase of the number of network outputs with the number of degrees of freedom by allowing a level of independence for each individual action dimension. To illustrate the approach, we present a novel agent, called Branching Dueling Q-Network (BDQ), as a branching variant of the Dueling Double Deep Q-Network (Dueling DDQN). We evaluate the performance of our agent on a set of challenging continuous control tasks. The empirical results show that the proposed agent scales gracefully to environments with increasing action dimensionality and indicate the significance of the shared decision module in coordination of the distributed action branches. Furthermore, we show that the proposed agent performs competitively against a state-of-the-art continuous control algorithm, Deep Deterministic Policy Gradient (DDPG).

Connected through hyperedge HE-1 (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"
    - "discrete sequential prediction of continuous actions for deep rl"
    - "distributed prioritized experience replay"
    - "sampled policy gradient for learning to play the game agar io"

Connected through hyperedge HE-2 (induced by source paper "scaling all goals updates in reinforcement learning using convolutional neural networks"):
  with members:
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "many goals reinforcement learning"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"
    - "time limits in reinforcement learning"
    - "deep exploration via bootstrapped dqn"
    - "exploration a study of count based exploration for deep reinforcement learning"
    - "value iteration networks"
    - "dueling network architectures for deep reinforcement learning"

Connected through hyperedge HE-3 (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-4 (induced by source paper "cesma centralized expert supervises multi agents"):
  with members:
    - "decentralization of multiagent policies by learning what to communicate"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "multi agent common knowledge reinforcement learning"
    - "counterfactual multi agent policy gradients"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "emergence of grounded compositional language in multi agent populations"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "value decomposition networks for cooperative multi agent learning"

Connected through hyperedge HE-5 (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-6 (induced by source paper "learning index selection with structured action spaces"):
  with members:
    - "learning action representations for reinforcement learning"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "the case for automatic database administration using deep reinforcement learning"
    - "darts differentiable architecture search"
    - "lift reinforcement learning in computer systems by learning from demonstrations"
    - "learning permutations with sinkhorn policy gradient"
    - "towards a hands free query optimizer through deep learning"
    - "rainbow combining improvements in deep reinforcement learning"

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"
    - "discrete sequential prediction of continuous actions for deep rl"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-8 (induced by source paper "reinforcement learning for multi objective optimization of online decisions in high dimensional systems"):
  with members:
    - "learning action representations for reinforcement learning"
    - "imitation learning with concurrent actions in 3d games"
    - "reproducibility of benchmarked deep reinforcement learning tasks for continuous control"
    - "overcoming exploration in reinforcement learning with demonstrations"
    - "one shot imitation learning"
    - "proximal policy optimization algorithms"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "prioritized experience replay"

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)