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: "the laplacian in rl learning representations with efficient approximations"
  Abstract: The smallest eigenvectors of the graph Laplacian are well-known to provide a succinct representation of the geometry of a weighted graph. In reinforcement learning (RL), where the weighted graph may be interpreted as the state transition process induced by a behavior policy acting on the environment, approximating the eigenvectors of the Laplacian provides a promising approach to state representation learning. However, existing methods for performing this approximation are ill-suited in general RL settings for two main reasons: First, they are computationally expensive, often requiring operations on large matrices. Second, these methods lack adequate justification beyond simple, tabular, finite-state settings. In this paper, we present a fully general and scalable method for approximating the eigenvectors of the Laplacian in a model-free RL context. We systematically evaluate our approach and empirically show that it generalizes beyond the tabular, finite-state setting. Even in tabular, finite-state settings, its ability to approximate the eigenvectors outperforms previous proposals. Finally, we show the potential benefits of using a Laplacian representation learned using our method in goal-achieving RL tasks, providing evidence that our technique can be used to significantly improve the performance of an RL agent.

Connected through hyperedge HE-1 (induced by source paper "option discovery in the absence of rewards with manifold analysis"):
  with members:
    - "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"
    - "a laplacian framework for option discovery in reinforcement learning"
    - "the option critic architecture"

Connected through hyperedge HE-2 (induced by source paper "search on the replay buffer bridging planning and reinforcement learning"):
  with members:
    - "self supervised learning of image embedding for continuous control"
    - "learning latent plans from play"
    - "universal planning networks"
    - "hierarchical reinforcement learning with hindsight"
    - "temporal difference models model free deep rl for model based control"
    - "model ensemble trust region policy optimization"
    - "learning navigation behaviors end to end with autorl"
    - "semi parametric topological memory for navigation"

Connected through hyperedge HE-3 (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"
    - "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"
    - "deep reinforcement learning and the deadly triad"

Connected through hyperedge HE-4 (induced by source paper "learned critical probabilistic roadmaps for robotic motion planning"):
  with members:
    - "lego leveraging experience in roadmap generation for sampling based planning"
    - "long range indoor navigation with prm rl"
    - "identifying critical regions for motion planning using auto generated saliency labels with convolutional neural networks"
    - "using local experiences for global motion planning"
    - "robot motion planning in learned latent spaces"
    - "learning implicit sampling distributions for motion planning"
    - "learning sampling distributions for robot motion planning"
    - "robust locally linear controllable embedding"

Connected through hyperedge HE-5 (induced by source paper "count based exploration with the successor representation"):
  with members:
    - "benchmarking bonus based exploration methods on the arcade learning environment"
    - "exploration by random network distillation"
    - "parameter space noise for exploration"
    - "eigenoption discovery through the deep successor representation"
    - "finite sample analyses for td 0 with function approximation"
    - "count based exploration with neural density models"
    - "count based exploration in feature space for reinforcement learning"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"

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)