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: "near optimal behavior via approximate state abstraction"
  Abstract: The combinatorial explosion that plagues planning and reinforcement learning (RL) algorithms can be moderated using state abstraction. Prohibitively large task representations can be condensed such that essential information is preserved, and consequently, solutions are tractably computable. However, exact abstractions, which treat only fully-identical situations as equivalent, fail to present opportunities for abstraction in environments where no two situations are exactly alike. In this work, we investigate approximate state abstractions, which treat nearly-identical situations as equivalent. We present theoretical guarantees of the quality of behaviors derived from four types of approximate abstractions. Additionally, we empirically demonstrate that approximate abstractions lead to reduction in task complexity and bounded loss of optimality of behavior in a variety of environments.

Connected through hyperedge HE-1 (induced by source paper "learning state abstractions for transfer in continuous control"):
  with members:
    - "state representation learning for control an overview"
    - "value prediction network"
    - "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"
    - "state of the art control of atari games using shallow reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "learning discrete state abstractions with deep variational inference"):
  with members:
    - "minatar an atari inspired testbed for more efficient reinforcement learning experiments"
    - "online abstraction with mdp homomorphisms for deep learning"
    - "transfer with model features in reinforcement learning"
    - "distral robust multitask reinforcement learning"
    - "deep variational information bottleneck"
    - "prioritized experience replay"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "from abstractions to natural languages for coordinating planning agents"):
  with members:
    - "translating neuralese"
    - "emergence of grounded compositional language in multi agent populations"
    - "learning multiagent communication with backpropagation"
    - "a paradigm for situated and goal driven language learning"
    - "synthesizing robust plans under incomplete domain models"
    - "the symbol grounding problem"

Connected through hyperedge HE-4 (induced by source paper "model primitive hierarchical lifelong reinforcement learning"):
  with members:
    - "information bottleneck and its applications in deep learning"
    - "continual lifelong learning with neural networks a review"
    - "norml no reward meta learning"
    - "dac the double actor critic architecture for learning options"
    - "reinforcement learning with competitive ensembles of information constrained primitives"
    - "deepmind control suite"
    - "count based exploration with the successor representation"
    - "safe option critic learning safety in the option critic architecture"

Connected through hyperedge HE-5 (induced by source paper "deepmdp learning continuous latent space models for representation learning"):
  with members:
    - "a comparative analysis of expected and distributional reinforcement learning"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "a geometric perspective on optimal representations for reinforcement learning"
    - "model based reinforcement learning for atari"
    - "the value function polytope in reinforcement learning"
    - "dopamine a research framework for deep reinforcement learning"
    - "combined reinforcement learning via abstract representations"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-6 (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-7 (induced by source paper "successor features combine elements of model free and model based reinforcement learning"):
  with members:
    - "deepmdp learning continuous latent space models for representation learning"
    - "combined reinforcement learning via abstract representations"
    - "learning the reward function for a misspecified model"
    - "lipschitz continuity in model based reinforcement learning"
    - "is q learning provably efficient"
    - "value prediction network"
    - "advantages and limitations of using successor features for transfer in reinforcement learning"
    - "imagination augmented agents for deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "successor features support model based and model free reinforcement learning"):
  with members:
    - "world models"
    - "lipschitz continuity in model based reinforcement learning"
    - "value prediction network"
    - "advantages and limitations of using successor features for transfer in reinforcement learning"
    - "imagination augmented agents for deep reinforcement learning"
    - "successor features for transfer in reinforcement learning"
    - "self correcting models for model based reinforcement learning"
    - "deep reinforcement learning with successor features for navigation across similar environments"

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)