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: "using deep reinforcement learning to learn high level policies on the atrias biped"
  Abstract: Learning controllers for bipedal robots is a challenging problem, often requiring expert knowledge and extensive tuning of parameters that vary in different situations. Recently, deep reinforcement learning has shown promise at automatically learning controllers for complex systems in simulation. This has been followed by a push towards learning controllers that can be transferred between simulation and hardware, primarily with the use of domain randomization. However, domain randomization can make the problem of finding stable controllers even more challenging, especially for underactuated bipedal robots. In this work, we explore whether policies learned in simulation can be transferred to hardware with the use of high-fidelity simulators and structured controllers. We learn a neural network policy which is a part of a more structured controller. While the neural network is learned in simulation, the rest of the controller stays fixed, and can be tuned by the expert as needed. We show that using this approach can greatly speed up the rate of learning in simulation, as well as enable transfer of policies between simulation and hardware. We present our results on an ATRIAS robot and explore the effect of action spaces and cost functions on the rate of transfer between simulation and hardware. Our results show that structured policies can indeed be learned in simulation and implemented on hardware successfully. This has several advantages, as the structure preserves the intuitive nature of the policy, and the neural network improves the performance of the hand-designed policy. In this way, we propose a way of using neural networks to improve expert designed controllers, while maintaining ease of understanding.

Connected through hyperedge HE-1 (induced by source paper "rapidly adaptable legged robots via evolutionary meta learning"):
  with members:
    - "es maml simple hessian free meta learning"
    - "active domain randomization"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "varibad a very good method for bayes adaptive deep rl via meta learning"
    - "multi agent manipulation via locomotion using hierarchical sim2real"
    - "norml no reward meta learning"
    - "learning generalizable locomotion skills with hierarchical reinforcement learning"
    - "learning agile and dynamic motor skills for legged robots"

Connected through hyperedge HE-2 (induced by source paper "iterative reinforcement learning based design of dynamic locomotion skills for cassie"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "learning symmetric and low energy locomotion"
    - "progressive reinforcement learning with distillation for multi skilled motion control"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "feedback control of a cassie bipedal robot walking standing and riding a segway"
    - "feedback control for cassie with deep reinforcement learning"
    - "neural probabilistic motor primitives for humanoid control"
    - "active deep q learning with demonstration"

Connected through hyperedge HE-3 (induced by source paper "gym ignition reproducible robotic simulations for reinforcement learning"):
  with members:
    - "modelling generalized forces with reinforcement learning for sim to real transfer"
    - "hierarchical reinforcement learning for quadruped locomotion"
    - "assessing transferability from simulation to reality for reinforcement learning"
    - "bayessim adaptive domain randomization via probabilistic inference for robotics simulators"
    - "iterative reinforcement learning based design of dynamic locomotion skills for cassie"
    - "gym gazebo2 a toolkit for reinforcement learning using ros 2 and gazebo"
    - "learning agile and dynamic motor skills for legged robots"
    - "deepmind control suite"

Connected through hyperedge HE-4 (induced by source paper "bayesian optimization in variational latent spaces with dynamic compression"):
  with members:
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "zero shot skill composition and simulation to real transfer by learning task representations"
    - "sim to real learning agile locomotion for quadruped robots"
    - "state representation learning for control an overview"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "feedback control of a cassie bipedal robot walking standing and riding a segway"

Connected through hyperedge HE-5 (induced by source paper "learning generalizable locomotion skills with hierarchical reinforcement learning"):
  with members:
    - "data efficient reinforcement learning for legged robots"
    - "curious ilqr resolving uncertainty in model based rl"
    - "mcp learning composable hierarchical control with multiplicative compositional policies"
    - "bayesian optimization in variational latent spaces with dynamic compression"
    - "learning agile and dynamic motor skills for legged robots"
    - "learning flexible and reusable locomotion primitives for a microrobot"
    - "sim to real learning agile locomotion for quadruped robots"
    - "soft actor critic algorithms and applications"

Connected through hyperedge HE-6 (induced by source paper "improved exploration through latent trajectory optimization in deep deterministic policy gradient"):
  with members:
    - "universal planning networks"
    - "deepmind control suite"
    - "end to end race driving with deep reinforcement learning"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "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 drive in a day"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"

Connected through hyperedge HE-7 (induced by source paper "teach biped robots to walk via gait principles and reinforcement learning with adversarial critics"):
  with members:
    - "iterative reinforcement learning based design of dynamic locomotion skills for cassie"
    - "learning agile and dynamic motor skills for legged robots"
    - "sequential decision fusion for environmental classification in assistive walking"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "grp model for sensorimotor learning"):
  with members:
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "sfv reinforcement learning of physical skills from videos"
    - "directed info gail learning hierarchical policies from unsegmented demonstrations using directed information"
    - "learning human behaviors from motion capture by adversarial imitation"

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)