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: "robot representing and reasoning with knowledge from reinforcement learning"
  Abstract: Reinforcement learning (RL) agents aim at learning by interacting with an environment, and are not designed for representing or reasoning with declarative knowledge. Knowledge representation and reasoning (KRR) paradigms are strong in declarative KRR tasks, but are ill-equipped to learn from such experiences. In this work, we integrate logical-probabilistic KRR with model-based RL, enabling agents to simultaneously reason with declarative knowledge and learn from interaction experiences. The knowledge from humans and RL is unified and used for dynamically computing task-specific planning models under potentially new environments. Experiments were conducted using a mobile robot working on dialog, navigation, and delivery tasks. Results show significant improvements, in comparison to existing model-based RL methods.

Connected through hyperedge HE-1 (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"
    - "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making"
    - "integrating human provided information into belief state representation using dynamic factorization"

Connected through hyperedge HE-2 (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"
    - "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making"
    - "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-3 (induced by source paper "knowledge based sequential decision making under uncertainty"):
  with members:
    - "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making"
    - "explaining explanations an overview of interpretability of machine learning"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "the fast downward planning system"

Connected through hyperedge HE-4 (induced by source paper "sdrl interpretable and data efficient deep reinforcement learningleveraging symbolic planning"):
  with members:
    - "strategic object oriented reinforcement learning"
    - "fast exploration with simplified models and approximately optimistic planning in model based reinforcement learning"
    - "programmatically interpretable reinforcement learning"
    - "hierarchical imitation and reinforcement learning"
    - "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making"
    - "explaining explanations an overview of interpretability of machine learning"
    - "eigenoption discovery through the deep successor representation"
    - "advantages and limitations of using successor features for transfer in reinforcement learning"

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)