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: "policies modulating trajectory generators"
  Abstract: We propose an architecture for learning complex controllable behaviors by having simple Policies Modulate Trajectory Generators (PMTG), a powerful combination that can provide both memory and prior knowledge to the controller. The result is a flexible architecture that is applicable to a class of problems with periodic motion for which one has an insight into the class of trajectories that might lead to a desired behavior. We illustrate the basics of our architecture using a synthetic control problem, then go on to learn speed-controlled locomotion for a quadrupedal robot by using Deep Reinforcement Learning and Evolutionary Strategies. We demonstrate that a simple linear policy, when paired with a parametric Trajectory Generator for quadrupedal gaits, can induce walking behaviors with controllable speed from 4-dimensional IMU observations alone, and can be learned in under 1000 rollouts. We also transfer these policies to a real robot and show locomotion with controllable forward velocity.

Connected through hyperedge HE-1 (induced by source paper "multiplicative controller fusion a hybrid navigation strategy for deployment in unknown environments"):
  with members:
    - "residual reactive navigation combining classical and learned navigation strategies for deployment in unknown environments"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "on evaluation of embodied navigation agents"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "learning with training wheels speeding up training with a simple controller for deep reinforcement learning"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "deep q learning from demonstrations"

Connected through hyperedge HE-2 (induced by source paper "reinforcement learning with chromatic networks for compact architecture search"):
  with members:
    - "non differentiable supervised learning with evolution strategies and hybrid methods"
    - "exploring randomly wired neural networks for image recognition"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "playing atari with six neurons"
    - "neural architecture search with reinforcement learning"
    - "compression of neural machine translation models via pruning"

Connected through hyperedge HE-3 (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"
    - "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"
    - "learning to simulate"

Connected through hyperedge HE-4 (induced by source paper "data efficient reinforcement learning for legged robots"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "when to trust your model model based policy optimization"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning latent dynamics for planning from pixels"
    - "feedback control for cassie with deep reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "learning to walk via deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "residual reactive navigation combining classical and learned navigation strategies for deployment in unknown environments"):
  with members:
    - "estimating risk and uncertainty in deep reinforcement learning"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "residual policy learning"
    - "on evaluation of embodied navigation agents"
    - "residual reinforcement learning for robot control"
    - "addressing function approximation error in actor critic methods"
    - "learning with training wheels speeding up training with a simple controller for deep reinforcement learning"
    - "towards monocular vision based obstacle avoidance through deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "mpc net a first principles guided policy search"):
  with members:
    - "feedback mpc for torque controlled legged robots"
    - "data efficient reinforcement learning for legged robots"
    - "iterative reinforcement learning based design of dynamic locomotion skills for cassie"
    - "on the convergence of adam and beyond"
    - "learning agile and dynamic motor skills for legged robots"
    - "an algorithmic perspective on imitation learning"
    - "sim to real learning agile locomotion for quadruped robots"
    - "learning to walk via deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "reinforcement learning with chromatic networks"):
  with members:
    - "weight agnostic neural networks"
    - "exploring randomly wired neural networks for image recognition"
    - "learning sparse networks using targeted dropout"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "playing atari with six neurons"
    - "efficient neural architecture search via parameter sharing"

Connected through hyperedge HE-8 (induced by source paper "provably robust blackbox optimization for reinforcement learning"):
  with members:
    - "variance reduction for evolution strategies via structured control variates"
    - "introduction to online convex optimization"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "ppo cma proximal policy optimization with covariance matrix adaptation"
    - "proximal policy optimization algorithms"
    - "sharper bounds for regression and low rank approximation with regularization"
    - "orthogonal random features"

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)