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: "a laplacian framework for option discovery in reinforcement learning"
  Abstract: Representation learning and option discovery are two of the biggest challenges in reinforcement learning (RL). Proto-value functions (PVFs) are a well-known approach for representation learning in MDPs. In this paper we address the option discovery problem by showing how PVFs implicitly define options. We do it by introducing eigenpurposes, intrinsic reward functions derived from the learned representations. The options discovered from eigenpurposes traverse the principal directions of the state space. They are useful for multiple tasks because they are discovered without taking the environment's rewards into consideration. Moreover, different options act at different time scales, making them helpful for exploration. We demonstrate features of eigenpurposes in traditional tabular domains as well as in Atari 2600 games.

Connected through hyperedge HE-1 (induced by source paper "options of interest temporal abstraction with interest functions"):
  with members:
    - "the termination critic"
    - "feudal networks for hierarchical reinforcement learning"
    - "learnings options end to end for continuous action tasks"
    - "when waiting is not an option learning options with a deliberation cost"
    - "adaptive skills adaptive partitions asap"
    - "option discovery in hierarchical reinforcement learning using spatio temporal clustering"
    - "the option critic architecture"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"

Connected through hyperedge HE-2 (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"
    - "eigenoption discovery through the deep successor representation"
    - "curiosity driven exploration by self supervised prediction"
    - "stochastic neural networks for hierarchical reinforcement learning"
    - "the option critic architecture"

Connected through hyperedge HE-3 (induced by source paper "learning reusable options for multi task reinforcement learning"):
  with members:
    - "when waiting is not an option learning options with a deliberation cost"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "the option critic architecture"
    - "asynchronous methods for deep reinforcement learning"

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 "reinforcement learning with competitive ensembles of information constrained primitives"):
  with members:
    - "routing networks and the challenges of modular and compositional computation"
    - "model primitive hierarchical lifelong reinforcement learning"
    - "hierarchical visuomotor control of humanoids"
    - "latent space policies for hierarchical reinforcement learning"
    - "diversity is all you need learning skills without a reward function"
    - "neural probabilistic motor primitives for humanoid control"
    - "inferring and executing programs for visual reasoning"
    - "meta learning shared hierarchies"

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"
    - "eigenoption discovery through the deep successor representation"
    - "feudal networks for hierarchical reinforcement learning"

Connected through hyperedge HE-7 (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"
    - "eigenoption discovery through the deep successor representation"
    - "feudal networks for hierarchical reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "sample efficient reinforcement learning with maximum entropy mellowmax episodic control"):
  with members:
    - "ucb exploration via q ensembles"
    - "a short variational proof of equivalence between policy gradients and soft q learning"
    - "deep reinforcement learning a brief survey"
    - "ucb and infogain exploration via boldsymbol q ensembles"
    - "bridging the gap between value and policy based reinforcement learning"
    - "a unified view of entropy regularized markov decision processes"
    - "deep exploration via bootstrapped dqn"
    - "building machines that learn and think like people"

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)