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: "option discovery in hierarchical reinforcement learning using spatio temporal clustering"
  Abstract: This paper introduces an automated skill acquisition framework in reinforcement learning which involves identifying a hierarchical description of the given task in terms of abstract states and extended actions between abstract states. Identifying such structures present in the task provides ways to simplify and speed up reinforcement learning algorithms. These structures also help to generalize such algorithms over multiple tasks without relearning policies from scratch. We use ideas from dynamical systems to find metastable regions in the state space and associate them with abstract states. The spectral clustering algorithm PCCA+ is used to identify suitable abstractions aligned to the underlying structure. Skills are defined in terms of the sequence of actions that lead to transitions between such abstract states. The connectivity information from PCCA+ is used to generate these skills or options. These skills are independent of the learning task and can be efficiently reused across a variety of tasks defined over the same model. This approach works well even without the exact model of the environment by using sample trajectories to construct an approximate estimate. We also present our approach to scaling the skill acquisition framework to complex tasks with large state spaces for which we perform state aggregation using the representation learned from an action conditional video prediction network and use the skill acquisition framework on the aggregated state space.

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"
    - "a laplacian framework for option discovery in reinforcement learning"
    - "adaptive skills adaptive partitions asap"
    - "the option critic architecture"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"

Connected through hyperedge HE-2 (induced by source paper "cooperation without coordination hierarchical predictive planning for decentralized multiagent navigation"):
  with members:
    - "long range indoor navigation with prm rl"
    - "r maddpg for partially observable environments and limited communication"
    - "rl rrt kinodynamic motion planning via learning reachability estimators from rl policies"
    - "actor attention critic for multi agent reinforcement learning"
    - "learning navigation behaviors end to end with autorl"
    - "getting robots unfrozen and unlost in dense pedestrian crowds"
    - "efficient hierarchical robot motion planning under uncertainty and hybrid dynamics"
    - "counterfactual multi agent policy gradients"

Connected through hyperedge HE-3 (induced by source paper "learning options from demonstration using skill segmentation"):
  with members:
    - "learning parameterized skills"

Connected through hyperedge HE-4 (induced by source paper "successor options an option discovery framework for reinforcement learning"):
  with members:
    - "count based exploration with the successor representation"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "eigenoption discovery through the deep successor representation"
    - "feudal networks for hierarchical reinforcement learning"
    - "proximal policy optimization algorithms"
    - "a laplacian framework for option discovery in reinforcement learning"
    - "the option critic architecture"
    - "successor features for transfer in reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "learning plannable representations with causal infogan"):
  with members:
    - "universal planning networks"
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "temporal difference models model free deep rl for model based control"
    - "semi parametric topological memory for navigation"
    - "hindsight experience replay"
    - "combining self supervised learning and imitation for vision based rope manipulation"
    - "visual interaction networks"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-6 (induced by source paper "learning representations in model free hierarchical reinforcement learning"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "feudal networks for hierarchical reinforcement learning"
    - "multi level discovery of deep options"
    - "a laplacian framework for option discovery in reinforcement learning"
    - "learning purposeful behaviour in the absence of rewards"
    - "the option critic architecture"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "unifying count based exploration and intrinsic motivation"

Connected through hyperedge HE-7 (induced by source paper "multi level discovery of deep options"):
  with members:
    - "learning to repeat fine grained action repetition for deep reinforcement learning"
    - "composing meta policies for autonomous driving using hierarchical deep reinforcement learning"
    - "stochastic neural networks for hierarchical reinforcement learning"
    - "learning from the memory of atari 2600"
    - "learning and transfer of modulated locomotor controllers"
    - "the option critic architecture"
    - "hierarchical linearly solvable markov decision problems"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"

Connected through hyperedge HE-8 (induced by source paper "a laplacian framework for option discovery in reinforcement learning"):
  with members:
    - "learning purposeful behaviour in the absence of rewards"
    - "the option critic architecture"
    - "strategic attentive writer for learning macro actions"
    - "control of memory active perception and action in minecraft"
    - "empowerment an introduction"
    - "the arcade learning environment an evaluation platform for general agents"
    - "active learning of inverse models with intrinsically motivated goal exploration in robots"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

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)