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: "a refinement based architecture for knowledge representation and reasoning in robotics"
  Abstract: This paper describes an architecture that combines the complementary strengths of probabilistic graphical models and declarative programming to enable robots to represent and reason with qualitative and quantitative descriptions of uncertainty and domain knowledge. The architecture comprises three components: a controller, a logician and an executor. The logician uses a coarse-resolution, qualitative model of the world and logical reasoning to plan a sequence of abstract actions for an assigned goal. For each abstract action to be executed, the executor uses probabilistic information at finer granularity and probabilistic algorithms to execute a sequence of concrete actions, and to report observations of the environment. The controller dispatches goals and relevant information to the logician and executor, collects and processes information from these components, refines the coarse-resolution description to obtain the probabilistic information at finer granularity for each abstract action, and computes the policy for executing the abstract action using probabilistic algorithms. The architecture is evaluated in simulation and on a mobile robot moving objects in an indoor domain, to show that it supports reasoning with violation of defaults, noisy observations and unreliable actions, in complex domains.

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 "desiderata for planning systems in general purpose service robots"):
  with members:
    - "what should i do now marrying reinforcement learning and symbolic planning"
    - "improving grounded natural language understanding through human robot dialog"
    - "interaction and autonomy in robocup home and building wide intelligence"
    - "integrating task motion planning with reinforcement learning for robust decision making in mobile robots"
    - "balancing explicability and explanation in human aware planning"
    - "the strands project long term autonomy in everyday environments"
    - "plan explanations as model reconciliation moving beyond explanation as soliloquy"
    - "plan explicability and predictability for robot task planning"

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"
    - "integrating human provided information into belief state representation using dynamic factorization"
    - "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.RO(Robotics)