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 policy learning"
  Abstract: We present Residual Policy Learning (RPL): a simple method for improving nondifferentiable policies using model-free deep reinforcement learning. RPL thrives in complex robotic manipulation tasks where good but imperfect controllers are available. In these tasks, reinforcement learning from scratch remains data-inefficient or intractable, but learning a residual on top of the initial controller can yield substantial improvements. We study RPL in six challenging MuJoCo tasks involving partial observability, sensor noise, model misspecification, and controller miscalibration. For initial controllers, we consider both hand-designed policies and model-predictive controllers with known or learned transition models. By combining learning with control algorithms, RPL can perform long-horizon, sparse-reward tasks for which reinforcement learning alone fails. Moreover, we find that RPL consistently and substantially improves on the initial controllers. We argue that RPL is a promising approach for combining the complementary strengths of deep reinforcement learning and robotic control, pushing the boundaries of what either can achieve independently. Video and code at https://k-r-allen.github.io/residual-policy-learning/.

Connected through hyperedge HE-1 (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"
    - "natural gradient deep q learning"
    - "off policy deep reinforcement learning without exploration"
    - "horizon facebook s open source applied reinforcement learning platform"
    - "residual reinforcement learning for robot control"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-2 (induced by source paper "elaborating on learned demonstrations with temporal logic specifications"):
  with members:
    - "specification patterns for robotic missions"
    - "disentangled relational representations for explaining and learning from demonstration"
    - "imitation learning of robot policies by combining language vision and demonstration"
    - "guiding policies with language via meta learning"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "interpretable apprenticeship learning with temporal logic specifications"
    - "generative adversarial imitation learning"
    - "robust linear temporal logic"

Connected through hyperedge HE-3 (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"
    - "learning dexterous in hand manipulation"
    - "off policy deep reinforcement learning without exploration"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "multi goal reinforcement learning challenging robotics environments and request for research"

Connected through hyperedge HE-4 (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"
    - "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"
    - "residual reinforcement learning for robot control"

Connected through hyperedge HE-5 (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"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "meta reinforcement learning of structured exploration strategies"

Connected through hyperedge HE-6 (induced by source paper "differentiable algorithm networks for composable robot learning"):
  with members:
    - "credit assignment techniques in stochastic computation graphs"
    - "universal planning networks"
    - "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"
    - "on evaluation of embodied navigation agents"

Connected through hyperedge HE-7 (induced by source paper "side tuning network adaptation via additive side networks"):
  with members:
    - "on the variance of the adaptive learning rate and beyond"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "superposition of many models into one"
    - "habitat a platform for embodied ai research"
    - "taskonomy disentangling task transfer learning"
    - "efficient lifelong learning with a gem"
    - "know what you don t know unanswerable questions for squad"
    - "residual reinforcement learning for robot control"

Connected through hyperedge HE-8 (induced by source paper "tunenet one shot residual tuning for system identification and sim to real robot task transfer"):
  with members:
    - "bayessim adaptive domain randomization via probabilistic inference for robotics simulators"
    - "densephysnet learning dense physical object representations via multi step dynamic interactions"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "friction variability in auto collected dataset of planar pushing experiments and anisotropic friction"
    - "sim to real learning agile locomotion for quadruped robots"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"
    - "residual reinforcement learning for robot 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)