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: "active model learning and diverse action sampling for task and motion planning"
  Abstract: The objective of this work is to augment the basic abilities of a robot by learning to use new sensorimotor primitives to enable the solution of complex long-horizon problems. Solving long-horizon problems in complex domains requires flexible generative planning that can combine primitive abilities in novel combinations to solve problems as they arise in the world. In order to plan to combine primitive actions, we must have models of the preconditions and effects of those actions: under what circumstances will executing this primitive achieve some particular effect in the world? #R##N#We use, and develop novel improvements on, state-of-the-art methods for active learning and sampling. We use Gaussian process methods for learning the conditions of operator effectiveness from small numbers of expensive training examples collected by experimentation on a robot. We develop adaptive sampling methods for generating diverse elements of continuous sets (such as robot configurations and object poses) during planning for solving a new task, so that planning is as efficient as possible. We demonstrate these methods in an integrated system, combining newly learned models with an efficient continuous-space robot task and motion planner to learn to solve long horizon problems more efficiently than was previously possible.

Connected through hyperedge HE-1 (induced by source paper "epsilon shotgun epsilon greedy batch bayesian optimisation"):
  with members:
    - "expected improvement versus predicted value in surrogate based optimization"
    - "sampling acquisition functions for batch bayesian optimization"
    - "greed is good exploration and exploitation trade offs in bayesian optimisation"
    - "efficient nonmyopic bayesian optimization and quadrature"
    - "bayesian optimization in alphago"
    - "parallel predictive entropy search for batch global optimization of expensive objective functions"

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"
    - "learning to guide task and motion planning using score space representation"
    - "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 "from semantics to execution integrating action planning with reinforcement learning for robotic causal problem solving"):
  with members:
    - "continual reinforcement learning deployed in real life using policy distillation and sim2real transfer"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "playing hard exploration games by watching youtube"
    - "observe and look further achieving consistent performance on atari"
    - "state representation learning for control an overview"
    - "curriculum goal masking for continuous deep reinforcement learning"
    - "hierarchical reinforcement learning with abductive planning"

Connected through hyperedge HE-4 (induced by source paper "batch active learning using determinantal point processes"):
  with members:
    - "diverse mini batch active learning"
    - "uncertainty sampling is preconditioned stochastic gradient descent on zero one loss"
    - "log concave polynomials ii high dimensional walks and an fpras for counting bases of a matroid"
    - "batch active preference based learning of reward functions"
    - "determinantal point processes for mini batch diversification"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "learning preferences for manipulation tasks from online coactive feedback"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"

Connected through hyperedge HE-5 (induced by source paper "anytime integrated task and motion policies for stochastic environments"):
  with members:
    - "sampling based methods for factored task and motion planning"
    - "stripstream integrating symbolic planners and blackbox samplers"
    - "an anytime algorithm for task and motion mdps"
    - "ffrob leveraging symbolic planning for efficient task and motion planning"
    - "deep reinforcement learning in parameterized action space"

Connected through hyperedge HE-6 (induced by source paper "residual policy learning"):
  with members:
    - "temporal difference models model free deep rl for model based control"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "residual reinforcement learning for robot control"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "hindsight experience replay"
    - "combining learned and analytical models for predicting action effects"

Connected through hyperedge HE-7 (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"
    - "learning to guide task and motion planning using score space representation"
    - "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"

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)