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: "combining model based and model free updates for trajectory centric reinforcement learning"
  Abstract: Reinforcement learning (RL) algorithms for real-world robotic applications need a data-efficient learning process and the ability to handle complex, unknown dynamical systems. These requirements are handled well by model-based and model-free RL approaches, respectively. In this work, we aim to combine the advantages of these two types of methods in a principled manner. By focusing on time-varying linear-Gaussian policies, we enable a model-based algorithm based on the linear quadratic regulator (LQR) that can be integrated into the model-free framework of path integral policy improvement (PI2). We can further combine our method with guided policy search (GPS) to train arbitrary parameterized policies such as deep neural networks. Our simulation and real-world experiments demonstrate that this method can solve challenging manipulation tasks with comparable or better performance than model-free methods while maintaining the sample efficiency of model-based methods. A video presenting our results is available at this https URL

Connected through hyperedge HE-1 (induced by source paper "multimodal trajectory optimization for motion planning"):
  with members:
    - "hierarchical reinforcement learning via advantage weighted information maximization"
    - "learning novel policies for tasks"
    - "an algorithmic perspective on imitation learning"
    - "diversity is all you need learning skills without a reward function"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "continuous time gaussian process motion planning via probabilistic inference"
    - "divide and conquer reinforcement learning"
    - "kernelized movement primitives"

Connected through hyperedge HE-2 (induced by source paper "robot playing kendama with model based and model free reinforcement learning"):
  with members:
    - "smooth and efficient policy exploration for robot trajectory learning"
    - "path integral guided policy search"

Connected through hyperedge HE-3 (induced by source paper "robonet large scale multi robot learning"):
  with members:
    - "self supervised exploration via disagreement"
    - "learning latent plans from play"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "manipulation by feel touch based control with deep predictive models"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "unsupervised visuomotor control through distributional planning networks"
    - "scaling autoregressive video models"
    - "exploration by random network distillation"

Connected through hyperedge HE-4 (induced by source paper "dynamics aware unsupervised discovery of skills"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "therml the thermodynamics of machine learning"
    - "model ensemble trust region policy optimization"
    - "soft actor critic algorithms and applications"
    - "diversity is all you need learning skills without a reward function"
    - "unsupervised control through non parametric discriminative rewards"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-5 (induced by source paper "visual reaction learning to play catch with your drone"):
  with members:
    - "relational action forecasting"
    - "high fidelity video prediction with large stochastic recurrent neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning latent dynamics for planning from pixels"
    - "model ensemble trust region policy optimization"
    - "semi parametric topological memory for navigation"
    - "learning and querying fast generative models for reinforcement learning"
    - "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation"

Connected through hyperedge HE-6 (induced by source paper "meta world a benchmark and evaluation for multi task and meta reinforcement learning"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "ray interference a source of plateaus in deep reinforcement learning"
    - "habitat a platform for embodied ai research"
    - "deepmind control suite"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "meta learning by the baldwin effect"

Connected through hyperedge HE-7 (induced by source paper "unsupervised visuomotor control through distributional planning networks"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "temporal difference models model free deep rl for model based control"
    - "learning actionable representations with goal conditioned policies"
    - "visual reinforcement learning with imagined goals"
    - "zero shot visual imitation"
    - "soft actor critic algorithms and applications"
    - "few shot goal inference for visuomotor learning and planning"
    - "unsupervised control through non parametric discriminative rewards"

Connected through hyperedge HE-8 (induced by source paper "a survey of behavior learning applications in robotics state of the art and perspectives"):
  with members:
    - "learning dexterous in hand manipulation"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "deep haptic model predictive control for robot assisted dressing"
    - "one shot visual imitation learning via meta learning"
    - "deep reinforcement learning a brief survey"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "time contrastive networks self supervised learning from video"
    - "robot composite learning and the nunchaku flipping challenge"

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)