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: "residual reinforcement learning for robot control"
  Abstract: Conventional feedback control methods can solve various types of robot control problems very efficiently by capturing the structure with explicit models, such as rigid body equations of motion. However, many control problems in modern manufacturing deal with contacts and friction, which are difficult to capture with first-order physical modeling. Hence, applying control design methodologies to these kinds of problems often results in brittle and inaccurate controllers, which have to be manually tuned for deployment. Reinforcement learning (RL) methods have been demonstrated to be capable of learning continuous robot controllers from interactions with the environment, even for problems that include friction and contacts. In this paper, we study how we can solve difficult control problems in the real world by decomposing them into a part that is solved efficiently by conventional feedback control methods, and the residual which is solved with RL. The final control policy is a superposition of both control signals. We demonstrate our approach by training an agent to successfully perform a real-world block assembly task involving contacts and unstable objects.

Connected through hyperedge HE-1 (induced by source paper "learning contact rich manipulation tasks with rigid position controlled robots learning to force control"):
  with members:
    - "learning variable impedance control for contact sensitive tasks"
    - "variable impedance control in end effector space an action space for reinforcement learning in contact rich tasks"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "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"
    - "learning robotic assembly from cad"
    - "a framework for robot manipulation skill formalism meta learning and adaptive control"

Connected through hyperedge HE-2 (induced by source paper "undefined behavior guarantee by switching to model based controller according to the embedded dynamics in recurrent neural network"):
  with members:
    - "online self supervised learning for object picking detecting optimum grasping position using a metric learning approach"
    - "control barrier functions theory and applications"
    - "grasp2vec learning object representations from self supervised grasping"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"
    - "motion switching with sensory and instruction signals by designing dynamical systems using deep neural network"
    - "machine teaching a new paradigm for building machine learning systems"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "generative adversarial imitation learning"

Connected through hyperedge HE-3 (induced by source paper "brpo batch residual policy optimization"):
  with members:
    - "benchmarking batch deep reinforcement learning algorithms"
    - "multi agent manipulation via locomotion using hierarchical sim2real"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "residual policy learning"
    - "natural gradient deep q learning"
    - "off policy deep reinforcement learning without exploration"
    - "horizon facebook s open source applied reinforcement learning platform"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-4 (induced by source paper "ppmc rl training algorithm rough terrain intelligent robots through reinforcement learning"):
  with members:
    - "data efficient reinforcement learning for legged robots"
    - "fast approximate clearance evaluation for rovers with articulated suspension systems"
    - "adaptive tensegrity locomotion on rough terrain via reinforcement learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "emergence of locomotion behaviours in rich environments"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"

Connected through hyperedge HE-5 (induced by source paper "ac teach a bayesian actor critic method for policy learning with an ensemble of suboptimal teachers"):
  with members:
    - "crossnorm normalization for off policy td reinforcement learning"
    - "towards characterizing divergence in deep q learning"
    - "reinforcement learning from imperfect demonstrations"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "residual policy learning"
    - "learning dexterous in hand manipulation"
    - "off policy deep reinforcement learning without exploration"
    - "reinforcement and imitation learning for diverse visuomotor skills"

Connected through hyperedge HE-6 (induced by source paper "deep reinforcement learning for industrial insertion tasks with visual inputs and natural reward signals"):
  with members:
    - "reinforcement learning on variable impedance controller for high precision robotic assembly"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "temporal difference models model free deep rl for model based control"
    - "residual policy learning"
    - "learning robotic assembly from cad"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "visual reinforcement learning with imagined goals"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"

Connected through hyperedge HE-7 (induced by source paper "bayesian residual policy optimization scalable bayesian reinforcement learning with clairvoyant experts"):
  with members:
    - "meta learning of sequential strategies"
    - "lets drive driving in a crowd by learning from tree search"
    - "meta learners learning dynamics are unlike learners"
    - "some considerations on learning to explore via meta reinforcement learning"
    - "exploration by random network distillation"
    - "residual policy learning"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "sim to real transfer of robotic control with dynamics randomization"

Connected through hyperedge HE-8 (induced by source paper "differentiable algorithm networks for composable robot learning"):
  with members:
    - "credit assignment techniques in stochastic computation graphs"
    - "universal planning networks"
    - "residual policy learning"
    - "learning dexterous in hand manipulation"
    - "mpc inspired neural network policies for sequential decision making"
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "learning to search with mctsnets"
    - "differentiable mpc for end to end planning and control"

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)