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: "compositional planning using optimal option models"
  Abstract: In this paper we introduce a framework for option model composition. Option models are temporal abstractions that, like macro-operators in classical planning, jump directly from a start state to an end state. Prior work has focused on constructing option models from primitive actions, by intra-option model learning; or on using option models to construct a value function, by inter-option planning. We present a unified view of intra- and inter-option model learning, based on a major generalisation of the Bellman equation. Our fundamental operation is the recursive composition of option models into other option models. This key idea enables compositional planning over many levels of abstraction. We illustrate our framework using a dynamic programming algorithm that simultaneously constructs optimal option models for multiple subgoals, and also searches over those option models to provide rapid progress towards other subgoals.

Connected through hyperedge HE-1 (induced by source paper "combating the compounding error problem with a multi step model"):
  with members:
    - "deepmdp learning continuous latent space models for representation learning"
    - "model ensemble trust region policy optimization"
    - "model based active exploration"
    - "lipschitz continuity in model based reinforcement learning"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"
    - "equivalence between wasserstein and value aware model based reinforcement learning"
    - "model based rl in contextual decision processes pac bounds and exponential improvements over model free approaches"
    - "multi step reinforcement learning a unifying algorithm"

Connected through hyperedge HE-2 (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-3 (induced by source paper "learning from trajectories via subgoal discovery"):
  with members:
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "learning abstract options"
    - "trajectory based learning for ball in maze games"
    - "investigating human priors for playing video games"
    - "fast policy learning through imitation and reinforcement"
    - "behavioral cloning from observation"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "deeply aggrevated differentiable imitation learning for sequential prediction"

Connected through hyperedge HE-4 (induced by source paper "learning robust options by conditional value at risk optimization"):
  with members:
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "soft robust actor critic policy gradient"
    - "learning robust options"
    - "benchmark environments for multitask learning in continuous domains"
    - "robust adversarial reinforcement learning"
    - "proximal policy optimization algorithms"
    - "the option critic architecture"
    - "epopt learning robust neural network policies using model ensembles"

Connected through hyperedge HE-5 (induced by source paper "planning with abstract learned models while learning transferable subtasks"):
  with members:
    - "an investigation of model free planning"
    - "learning representations in model free hierarchical reinforcement learning"
    - "learning multi level hierarchies with hindsight"
    - "meta learning shared hierarchies"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

Connected through hyperedge HE-6 (induced by source paper "learning abstract options"):
  with members:
    - "automated curriculum learning for neural networks"
    - "learning to compose skills"
    - "progressive neural networks"
    - "routing networks adaptive selection of non linear functions for multi task learning"
    - "feudal networks for hierarchical reinforcement learning"
    - "learning to multi task by active sampling"
    - "learnings options end to end for continuous action tasks"
    - "representation stability as a regularizer for improved text analytics transfer learning"

Connected through hyperedge HE-7 (induced by source paper "improving on policy learning with statistical reward accumulation"):
  with members:
    - "hindsight experience replay"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "a distributional perspective on reinforcement learning"
    - "hybrid reward architecture for reinforcement learning"
    - "distral robust multitask reinforcement learning"
    - "proximal policy optimization algorithms"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "sample efficient actor critic with experience replay"

Connected through hyperedge HE-8 (induced by source paper "finding options that minimize planning time"):
  with members:
    - "diversity is all you need learning skills without a reward function"
    - "when waiting is not an option learning options with a deliberation cost"
    - "a laplacian framework for option discovery in reinforcement learning"
    - "constructing abstraction hierarchies using a skill symbol loop"
    - "on the complexity of solving markov decision problems"
    - "analytical approach to parallel repetition"
    - "the complexity of plan existence and evaluation in probabilistic domains"
    - "label cover instances with large girth and the hardness of approximating basic k spanner"

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.AI(Artificial Intelligence)