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: "comparing task simplifications to learn closed loop object picking using deep reinforcement learning"
  Abstract: Enabling autonomous robots to interact in unstructured environments with dynamic objects requires manipulation capabilities that can deal with clutter, changes, and objects' variability. This paper presents a comparison of different reinforcement learning-based approaches for object picking with a robotic manipulator. We learn closed-loop policies mapping depth camera inputs to motion commands and compare different approaches to keep the problem tractable, including reward shaping, curriculum learning and using a policy pre-trained on a task with a reduced action set to warm-start the full problem. For efficient and more flexible data collection, we train in simulation and transfer the policies to a real robot. We show that using curriculum learning, policies learned with a sparse reward formulation can be trained at similar rates as with a shaped reward. These policies result in success rates comparable to the policy initialized on the simplified task. We could successfully transfer these policies to the real robot with only minor modifications of the depth image filtering. We found that using a heuristic to warm-start the training was useful to enforce desired behavior, while the policies trained from scratch using a curriculum learned better to cope with unseen scenarios where objects are removed.

Connected through hyperedge HE-1 (induced by source paper "whole body control of a mobile manipulator using end to end reinforcement learning"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "object finding in cluttered scenes using interactive perception"
    - "deep value model predictive control"
    - "whole body mpc for a dynamically stable mobile manipulator"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "sim to real learning agile locomotion for quadruped robots"
    - "benchmarking reinforcement learning algorithms on real world robots"
    - "nonlinear model predictive guidance for fixed wing uavs using identified control augmented dynamics"

Connected through hyperedge HE-2 (induced by source paper "qibullet a bullet based simulator for the pepper and nao robots"):
  with members:
    - "self supervised body image acquisition using a deep neural network for sensorimotor prediction"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "when random search is not enough sample efficient and noise robust blackbox optimization of rl policies"
    - "sim to real learning agile locomotion for quadruped robots"
    - "webotstm professional mobile robot simulation"

Connected through hyperedge HE-3 (induced by source paper "developmental bayesian optimization of black box with visual similarity based transfer learning"):
  with members:
    - "learning flexible and reusable locomotion primitives for a microrobot"
    - "virtual vs real trading off simulations and physical experiments in reinforcement learning with bayesian optimization"
    - "unscented bayesian optimization for safe robot grasping"
    - "symbol emergence in robotics a survey"
    - "robots that can adapt like animals"
    - "3d shapenets a deep representation for volumetric shapes"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical 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.RO(Robotics)