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: "eigenoption discovery through the deep successor representation"
  Abstract: Options in reinforcement learning allow agents to hierarchically decompose a task into subtasks, having the potential to speed up learning and planning. However, autonomously learning effective sets of options is still a major challenge in the field. In this paper we focus on the recently introduced idea of using representation learning methods to guide the option discovery process. Specifically, we look at eigenoptions, options obtained from representations that encode diffusive information flow in the environment. We extend the existing algorithms for eigenoption discovery to settings with stochastic transitions and in which handcrafted features are not available. We propose an algorithm that discovers eigenoptions while learning non-linear state representations from raw pixels. It exploits recent successes in the deep reinforcement learning literature and the equivalence between proto-value functions and the successor representation. We use traditional tabular domains to provide intuition about our approach and Atari 2600 games to demonstrate its potential.

Connected through hyperedge HE-1 (induced by source paper "option discovery in the absence of rewards with manifold analysis"):
  with members:
    - "the laplacian in rl learning representations with efficient approximations"
    - "spectral embedding norm looking deep into the spectrum of the graph laplacian"
    - "diversity is all you need learning skills without a reward function"
    - "q learning with nearest neighbors"
    - "curiosity driven exploration by self supervised prediction"
    - "stochastic neural networks for hierarchical reinforcement learning"
    - "a laplacian framework for option discovery in reinforcement learning"
    - "the option critic architecture"

Connected through hyperedge HE-2 (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-3 (induced by source paper "task agnostic dynamics priors for deep reinforcement learning"):
  with members:
    - "a study on overfitting in deep reinforcement learning"
    - "investigating human priors for playing video games"
    - "flexible neural representation for physics prediction"
    - "decoupling dynamics and reward for transfer learning"
    - "model based value estimation for efficient model free reinforcement learning"
    - "gotta learn fast a new benchmark for generalization in rl"
    - "visual interaction networks"
    - "independently controllable factors"

Connected through hyperedge HE-4 (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-5 (induced by source paper "scheduled intrinsic drive a hierarchical take on intrinsically motivated exploration"):
  with members:
    - "count based exploration with the successor representation"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "large scale study of curiosity driven learning"
    - "distributed prioritized experience replay"
    - "episodic curiosity through reachability"
    - "feudal networks for hierarchical reinforcement learning"
    - "count based exploration with neural density models"

Connected through hyperedge HE-6 (induced by source paper "dac the double actor critic architecture for learning options"):
  with members:
    - "generalized off policy actor critic"
    - "deepmind control suite"
    - "learning abstract options"
    - "learning latent dynamics for planning from pixels"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "ace an actor ensemble algorithm for continuous control with tree search"
    - "feudal networks for hierarchical reinforcement learning"
    - "efficient parallel methods for deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "hybrid reinforcement learning with expert state sequences"):
  with members:
    - "playing hard exploration games by watching youtube"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "behavioral cloning from observation"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "visual question generation as dual task of visual question answering"
    - "observational learning by reinforcement learning"
    - "third person imitation learning"

Connected through hyperedge HE-8 (induced by source paper "why does hierarchy sometimes work so well in reinforcement learning"):
  with members:
    - "multi agent manipulation via locomotion using hierarchical sim2real"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "rainbow combining improvements in deep reinforcement learning"
    - "parameter space noise for exploration"
    - "meta learning shared hierarchies"
    - "feudal networks for hierarchical reinforcement learning"
    - "noisy networks for exploration"

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)