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: "state of the art control of atari games using shallow reinforcement learning"
  Abstract: The recently introduced Deep Q-Networks (DQN) algorithm has gained attention as one of the first successful combinations of deep neural networks and reinforcement learning. Its promise was demonstrated in the Arcade Learning Environment (ALE), a challenging framework composed of dozens of Atari 2600 games used to evaluate general competency in AI. It achieved dramatically better results than earlier approaches, showing that its ability to learn good representations is quite robust and general. This paper attempts to understand the principles that underlie DQN's impressive performance and to better contextualize its success. We systematically evaluate the importance of key representational biases encoded by DQN's network by proposing simple linear representations that make use of these concepts. Incorporating these characteristics, we obtain a computationally practical feature set that achieves competitive performance to DQN in the ALE. Besides offering insight into the strengths and weaknesses of DQN, we provide a generic representation for the ALE, significantly reducing the burden of learning a representation for each game. Moreover, we also provide a simple, reproducible benchmark for the sake of comparison to future work in the ALE.

Connected through hyperedge HE-1 (induced by source paper "off policy deep reinforcement learning with analogous disentangled exploration"):
  with members:
    - "mulex disentangling exploitation from exploration in deep rl"
    - "importance resampling for off policy prediction"
    - "off policy deep reinforcement learning without exploration"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "soft actor critic algorithms and applications"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"

Connected through hyperedge HE-2 (induced by source paper "self supervised object level deep reinforcement learning"):
  with members:
    - "space unsupervised object oriented scene representation via spatial attention and decomposition"
    - "entity abstraction in visual model based reinforcement learning"
    - "unsupervised state representation learning in atari"
    - "model based reinforcement learning for atari"
    - "multi object representation learning with iterative variational inference"
    - "feature based aggregation and deep reinforcement learning a survey and some new implementations"
    - "object sensitive deep reinforcement learning"
    - "unsupervised video object segmentation for deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "learning state abstractions for transfer in continuous control"):
  with members:
    - "state representation learning for control an overview"
    - "value prediction network"
    - "near optimal behavior via approximate state abstraction"
    - "distral robust multitask reinforcement learning"
    - "learning to reinforcement learn"
    - "a deep hierarchical approach to lifelong learning in minecraft"
    - "successor features for transfer in reinforcement learning"
    - "a vector contraction inequality for rademacher complexities"

Connected through hyperedge HE-4 (induced by source paper "leaky tiling activations a simple approach to learning sparse representations online"):
  with members:
    - "meta learning representations for continual learning"
    - "learning sparse representations in reinforcement learning"
    - "learning action representations for reinforcement learning"
    - "representation learning on graphs a reinforcement learning application"
    - "continual state representation learning for reinforcement learning using generative replay"
    - "two geometric input transformation methods for fast online reinforcement learning with neural nets"
    - "deep reinforcement learning and the deadly triad"
    - "the utility of sparse representations for control in reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "a geometric perspective on optimal representations for reinforcement learning"):
  with members:
    - "deepmdp learning continuous latent space models for representation learning"
    - "the value function polytope in reinforcement learning"
    - "the laplacian in rl learning representations with efficient approximations"
    - "dopamine a research framework for deep reinforcement learning"
    - "feature based aggregation and deep reinforcement learning a survey and some new implementations"
    - "combined reinforcement learning via abstract representations"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "an atari model zoo for analyzing visualizing and comparing deep reinforcement learning agents"

Connected through hyperedge HE-6 (induced by source paper "harnessing structures for value based planning and reinforcement learning"):
  with members:
    - "me net towards effective adversarial robustness with matrix estimation"
    - "deepmind control suite"
    - "model agnostic time series analysis via matrix estimation"
    - "implicit quantile networks for distributional reinforcement learning"
    - "count based exploration with neural density models"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "high dimensional stochastic optimal control using continuous tensor decompositions"
    - "deep exploration via bootstrapped dqn"

Connected through hyperedge HE-7 (induced by source paper "deep policies for width based planning in pixel domains"):
  with members:
    - "beyond the one step greedy approach in reinforcement learning"
    - "planning with pixels in almost real time"
    - "improving width based planning with compact policies"
    - "count based exploration with neural density models"
    - "count based exploration in feature space for reinforcement learning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"
    - "unifying count based exploration and intrinsic motivation"

Connected through hyperedge HE-8 (induced by source paper "learning to play general video games via an object embedding network"):
  with members:
    - "investigating human priors for playing video games"
    - "deep sets"
    - "general video game ai a multi track framework for evaluating agents games and content generation algorithms"
    - "grounding language for transfer in deep reinforcement learning"
    - "visual interaction networks"
    - "a simple neural network module for relational reasoning"
    - "hybrid reward architecture for reinforcement learning"
    - "neural episodic control"

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)