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: "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making"
  Abstract: Reinforcement learning and symbolic planning have both been used to build intelligent autonomous agents. Reinforcement learning relies on learning from interactions with real world, which often requires an unfeasibly large amount of experience. Symbolic planning relies on manually crafted symbolic knowledge, which may not be robust to domain uncertainties and changes. In this paper we present a unified framework {\em PEORL} that integrates symbolic planning with hierarchical reinforcement learning (HRL) to cope with decision-making in a dynamic environment with uncertainties. #R##N#Symbolic plans are used to guide the agent's task execution and learning, and the learned experience is fed back to symbolic knowledge to improve planning. This method leads to rapid policy search and robust symbolic plans in complex domains. The framework is tested on benchmark domains of HRL.

Connected through hyperedge HE-1 (induced by source paper "transferable task execution from pixels through deep planning domain learning"):
  with members:
    - "representing robot task plans as robust logical dynamical systems"
    - "prospection interpretable plans from language by predicting the future"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "stripstream integrating symbolic planners and blackbox samplers"
    - "one shot hierarchical imitation learning of compound visuomotor tasks"
    - "visual robot task planning"
    - "learning generalized reactive policies using deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "integrating acting planning and learning in hierarchical operational models"):
  with members:
    - "learning in pomdps with monte carlo tree search"
    - "towards deep symbolic reinforcement learning"
    - "learning of behavior trees for autonomous agents"
    - "reinforcement learning a survey"

Connected through hyperedge HE-3 (induced by source paper "learning and reasoning for robot sequential decision making under uncertainty"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "enabling robots to understand incomplete natural language instructions using commonsense reasoning"
    - "from recognition to cognition visual commonsense reasoning"
    - "spatial knowledge distillation to aid visual reasoning"
    - "iterative visual reasoning beyond convolutions"
    - "sdrl interpretable and data efficient deep reinforcement learningleveraging symbolic planning"
    - "robot representing and reasoning with knowledge from reinforcement learning"
    - "integrating human provided information into belief state representation using dynamic factorization"

Connected through hyperedge HE-4 (induced by source paper "incremental learning of discrete planning domains from continuous perceptions"):
  with members:
    - "learning plannable representations with causal infogan"
    - "pomdp model learning for human robot collaboration"
    - "learning in pomdps with monte carlo tree search"
    - "self consistent trajectory autoencoder hierarchical reinforcement learning with trajectory embeddings"
    - "classical planning in deep latent space bridging the subsymbolic symbolic boundary"
    - "learning strips operators from noisy and incomplete observations"
    - "learning symbolic models of stochastic domains"
    - "reinforcement learning a survey"

Connected through hyperedge HE-5 (induced by source paper "bridging commonsense reasoning and probabilistic planning via a probabilistic action language"):
  with members:
    - "sdrl interpretable and data efficient deep reinforcement learningleveraging symbolic planning"
    - "robot representing and reasoning with knowledge from reinforcement learning"
    - "a probabilistic extension of action language bc"
    - "integrating human provided information into belief state representation using dynamic factorization"
    - "integrating task motion planning with reinforcement learning for robust decision making in mobile robots"
    - "computing lpmln using asp and mln solvers"
    - "probabilistic reasoning with answer sets"

Connected through hyperedge HE-6 (induced by source paper "reinbo machine learning pipeline search and configuration with bayesian optimization embedded reinforcement learning"):
  with members:
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "neural architecture search with reinforcement learning"
    - "designing neural network architectures using reinforcement learning"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

Connected through hyperedge HE-7 (induced by source paper "a human centered data driven planner actor critic architecture via logic programming"):
  with members:
    - "interactive learning from policy dependent human feedback"
    - "modular action language"
    - "high dimensional continuous control using generalized advantage estimation"
    - "trust region policy optimization"
    - "the fast downward planning system"

Connected through hyperedge HE-8 (induced by source paper "elaboration tolerant representation of markov decision process via decision theoretic extension of probabilistic action language pbc"):
  with members:
    - "a probabilistic extension of action language bc"
    - "computing lpmln using asp and mln solvers"
    - "answer set programming for non stationary markov decision processes"
    - "probabilistic reasoning with answer sets"

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)