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: "integrating human provided information into belief state representation using dynamic factorization"
  Abstract: In partially observed environments, it can be useful for a human to provide the robot with declarative information that augments its direct sensory observations. For instance, given a robot on a search-and-rescue mission, a human operator might suggest locations of interest. We provide a representation for the robot's internal knowledge that supports efficient combination of raw sensory information with high-level declarative information presented in a formal language. Computational efficiency is achieved by dynamically selecting an appropriate factoring of the belief state, combining aspects of the belief when they are correlated through information and separating them when they are not. This strategy works in open domains, in which the set of possible objects is not known in advance, and provides significant improvements in inference time, leading to more efficient planning for complex partially observable tasks. We validate our approach experimentally in two open-domain planning problems: a 2D discrete gridworld task and a 3D continuous cooking task.

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"
    - "robot representing and reasoning with knowledge from reinforcement learning"
    - "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making"

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"
    - "robot representing and reasoning with knowledge from reinforcement learning"
    - "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making"
    - "a probabilistic extension of action language bc"
    - "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 "robot representing and reasoning with knowledge from reinforcement learning"):
  with members:
    - "relational deep reinforcement learning"
    - "task planning in robotics an empirical comparison of pddl based and asp based systems"
    - "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making"
    - "a refinement based architecture for knowledge representation and reasoning in robotics"
    - "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.AI(Artificial Intelligence)