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: "reinforcement learning for pivoting task"
  Abstract: In this work we propose an approach to learn a robust policy for solving the pivoting task. Recently, several model-free continuous control algorithms were shown to learn successful policies withou ...

Connected through hyperedge HE-1 (induced by source paper "balance between efficient and effective learning dense2sparse reward shaping for robot manipulation with environment uncertainty"):
  with members:
    - "contextual imagined goals for self supervised robotic learning"
    - "reward shaping via meta learning"
    - "dynamical distance learning for unsupervised and semi supervised skill discovery"
    - "motion perception in reinforcement learning with dynamic objects"
    - "deep reinforcement learning for industrial insertion tasks with visual inputs and natural reward signals"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "reinforcement learning with perturbed rewards"
    - "sim to real transfer of robotic control with dynamics randomization"

Connected through hyperedge HE-2 (induced by source paper "robust visual domain randomization for reinforcement learning"):
  with members:
    - "active domain randomization"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "measuring and characterizing generalization in deep reinforcement learning"
    - "generalization and regularization in dqn"
    - "a study on overfitting in deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "benchmarking in hand manipulation"):
  with members:
    - "learning dexterous in hand manipulation"
    - "dexterous manipulation graphs"
    - "pneumatic shape shifting fingers to reorient and grasp"
    - "relaxed rigidity constraints kinematic trajectory optimization and collision avoidance for in grasp manipulation"
    - "in hand manipulation via motion cones"
    - "geometric in hand regrasp planning alternating optimization of finger gaits and in grasp manipulation"
    - "reinforcement learning for non prehensile manipulation transfer from simulation to physical system"
    - "stable prehensile pushing in hand manipulation with alternating sticking contacts"

Connected through hyperedge HE-4 (induced by source paper "solving rubik s cube with a robot hand"):
  with members:
    - "sampling based planning of in hand manipulation with external pushes"
    - "active domain randomization"
    - "meta reinforcement learning with task embedding and shared policy"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "distilling policy distillation"
    - "deep dynamics models for learning dexterous manipulation"
    - "meta reinforcement learning as task inference"
    - "deceptionnet network driven domain randomization"

Connected through hyperedge HE-5 (induced by source paper "generalization in reinforcement learning with selective noise injection and information bottleneck"):
  with members:
    - "rogue gym a new challenge for generalization in reinforcement learning"
    - "deep reinforcement learning on a budget 3d control and reasoning without a supercomputer"
    - "obstacle tower a generalization challenge in vision control and planning"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "natural environment benchmarks for reinforcement learning"
    - "a study on overfitting in deep reinforcement learning"
    - "structured control nets for deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "from video game to real robot the transfer between action spaces"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "learning dexterous in hand manipulation"
    - "torille learning environment for hand to hand combat"
    - "sim to real learning agile locomotion for quadruped robots"
    - "progressive neural networks"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "starcraft ii a new challenge for reinforcement learning"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"

Connected through hyperedge HE-7 (induced by source paper "robust domain randomization for reinforcement learning"):
  with members:
    - "active domain randomization"
    - "unsupervised data augmentation for consistency training"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "measuring and characterizing generalization in deep reinforcement learning"
    - "generalization and regularization in dqn"
    - "transfer learning for related reinforcement learning tasks via image to image translation"
    - "closing the sim to real loop adapting simulation randomization with real world experience"

Connected through hyperedge HE-8 (induced by source paper "sim to multi real transfer of low level robust control policies to multiple quadrotors"):
  with members:
    - "neuroflight next generation flight control firmware"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "sim to real learning agile locomotion for quadruped robots"
    - "synthetically trained neural networks for learning human readable plans from real world demonstrations"
    - "reinforcement learning for non prehensile manipulation transfer from simulation to physical system"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "transferring end to end visuomotor control from simulation to real world for a multi stage task"
    - "control of a quadrotor with 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)