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: "learning to guide task and motion planning using score space representation"
  Abstract: In this paper, we propose a learning algorithm that speeds up the search in task and motion planning problems. Our algorithm proposes solutions to three different challenges that arise in learning to improve planning efficiency: what to predict, how to represent a planning problem instance, and how to transfer knowledge from one problem instance to another. We propose a method that predicts constraints on the search space based on a generic representation of a planning problem instance, called score-space, where we represent a problem instance in terms of the performance of a set of solutions attempted so far. Using this representation, we transfer knowledge, in the form of constraints, from previous problems based on the similarity in score space. We design a sequential algorithm that efficiently predicts these constraints, and evaluate it in three different challenging task and motion planning problems. Results indicate that our approach performs orders of magnitudes faster than an unguided planner

Connected through hyperedge HE-1 (induced by source paper "task motion planning for safe and efficient urban driving"):
  with members:
    - "interpretable end to end urban autonomous driving with latent deep reinforcement learning"
    - "model free deep reinforcement learning for urban autonomous driving"
    - "learning from interventions using hierarchical policies for safe learning"
    - "augmenting knowledge through statistical goal oriented human robot dialog"
    - "ffrob leveraging symbolic planning for efficient task and motion planning"
    - "carla an open urban driving simulator"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"

Connected through hyperedge HE-2 (induced by source paper "efficient bimanual manipulation using learned task schemas"):
  with members:
    - "pyrobot an open source robotics framework for research and benchmarking"
    - "hierarchical approaches for reinforcement learning in parameterized action space"
    - "active model learning and diverse action sampling for task and motion planning"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "meta learning shared hierarchies"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"

Connected through hyperedge HE-3 (induced by source paper "using local experiences for global motion planning"):
  with members:
    - "learning sampling distributions for robot motion planning"
    - "benchmarking motion planning algorithms an extensible infrastructure for analysis and visualization"
    - "experience based planning with sparse roadmap spanners"

Connected through hyperedge HE-4 (induced by source paper "learning quickly to plan quickly using modular meta learning"):
  with members:
    - "modular meta learning"
    - "sampling based methods for factored task and motion planning"
    - "active model learning and diverse action sampling for task and motion planning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "modular multitask reinforcement learning with policy sketches"
    - "neural module networks"
    - "adam a method for stochastic optimization"
    - "learning symbolic models of stochastic domains"

Connected through hyperedge HE-5 (induced by source paper "active model learning and diverse action sampling for task and motion planning"):
  with members:
    - "strips planning in infinite domains"
    - "visual closed loop control for pouring liquids"

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)