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: "deep reinforcement learning for tensegrity robot locomotion"
  Abstract: Tensegrity robots, composed of rigid rods connected by elastic cables, have a number of unique properties that make them appealing for use as planetary exploration rovers. However, control of tensegrity robots remains a difficult problem due to their unusual structures and complex dynamics. In this work, we show how locomotion gaits can be learned automatically using a novel extension of mirror descent guided policy search (MDGPS) applied to periodic locomotion movements, and we demonstrate the effectiveness of our approach on tensegrity robot locomotion. We evaluate our method with real-world and simulated experiments on the SUPERball tensegrity robot, showing that the learned policies generalize to changes in system parameters, unreliable sensor measurements, and variation in environmental conditions, including varied terrains and a range of different gravities. Our experiments demonstrate that our method not only learns fast, power-efficient feedback policies for rolling gaits, but that these policies can succeed with only the limited onboard sensing provided by SUPERball's accelerometers. We compare the learned feedback policies to learned open-loop policies and hand-engineered controllers, and demonstrate that the learned policy enables the first continuous, reliable locomotion gait for the real SUPERball robot. Our code and other supplementary materials are available from this http URL

Connected through hyperedge HE-1 (induced by source paper "how to pick the domain randomization parameters for sim to real transfer of reinforcement learning policies"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "when random search is not enough sample efficient and noise robust blackbox optimization of rl policies"
    - "policies modulating trajectory generators"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "supervised policy update"
    - "learning dexterous in hand manipulation"
    - "policy transfer with strategy optimization"
    - "sim to real transfer of robotic control with dynamics randomization"

Connected through hyperedge HE-2 (induced by source paper "a data efficient framework for training and sim to real transfer of navigation policies"):
  with members:
    - "universal planning networks"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "bayesian model agnostic meta learning"
    - "sim to real learning agile locomotion for quadruped robots"
    - "evolved policy gradients"
    - "probabilistic model agnostic meta learning"
    - "adversarial discriminative sim to real transfer of visuo motor policies"

Connected through hyperedge HE-3 (induced by source paper "solar deep structured latent representations for model based reinforcement learning"):
  with members:
    - "variational inverse control with events a general framework for data driven reward definition"
    - "learning latent dynamics for planning from pixels"
    - "learning dexterous in hand manipulation"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "state representation learning for control an overview"
    - "addressing function approximation error in actor critic methods"
    - "model based value estimation for efficient model free reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "deep reinforcement learning for gliding and perching bodies"):
  with members:
    - "remember and forget for experience replay"
    - "proximal policy optimization algorithms"
    - "sample efficient actor critic with experience replay"
    - "continuous deep q learning with model based acceleration"
    - "high dimensional continuous control using generalized advantage estimation"
    - "end to end training of deep visuomotor policies"
    - "off policy actor critic"
    - "reinforcement learning a survey"

Connected through hyperedge HE-5 (induced by source paper "efficient model identification for tensegrity locomotion"):
  with members:
    - "preparing for the unknown learning a universal policy with online system identification"
    - "virtual vs real trading off simulations and physical experiments in reinforcement learning with bayesian optimization"
    - "sample efficient optimization for learning controllers for bipedal locomotion"
    - "a convex polynomial force motion model for planar sliding identification and application"

Connected through hyperedge HE-6 (induced by source paper "model predictive control with reference input tracking for tensegrity spine robots"):
  with members:
    - "design simulation and testing of a flexible actuated spine for quadruped robots"
    - "state estimation for tensegrity robots"

Connected through hyperedge HE-7 (induced by source paper "adaptive tensegrity locomotion on rough terrain via reinforcement learning"):
  with members:
    - "inclined surface locomotion strategies for spherical tensegrity robots"

Connected through hyperedge HE-8 (induced by source paper "inverse kinematics for control of tensegrity soft robots existence and optimality of solutions"):
  with members:
    - "model predictive control with reference input tracking for tensegrity spine robots"

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)