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: "feedback control for cassie with deep reinforcement learning"
  Abstract: Bipedal locomotion skills are challenging to develop. Control strategies often use local linearization of the dynamics in conjunction with reduced-order abstractions to yield tractable solutions. In these model-based control strategies, the controller is often not fully aware of many details, including torque limits, joint limits, and other non-linearities that are necessarily excluded from the control computations for simplicity. Deep reinforcement learning (DRL) offers a promising model-free approach for controlling bipedal locomotion which can more fully exploit the dynamics. However, current results in the machine learning literature are often based on ad-hoc simulation models that are not based on corresponding hardware. Thus it remains unclear how well DRL will succeed on realizable bipedal robots. In this paper, we demonstrate the effectiveness of DRL using a realistic model of Cassie, a bipedal robot. By formulating a feedback control problem as finding the optimal policy for a Markov Decision Process, we are able to learn robust walking controllers that imitate a reference motion with DRL. Controllers for different walking speeds are learned by imitating simple time-scaled versions of the original reference motion. Controller robustness is demonstrated through several challenging tests, including sensory delay, walking blindly on irregular terrain and unexpected pushes at the pelvis. We also show we can interpolate between individual policies and that robustness can be improved with an interpolated policy.

Connected through hyperedge HE-1 (induced by source paper "learning to walk in the real world with minimal human effort"):
  with members:
    - "safety guided deep reinforcement learning via online gaussian process estimation"
    - "solving rubik s cube with a robot hand"
    - "generalizing from a few environments in safety critical reinforcement learning"
    - "data efficient reinforcement learning for legged robots"
    - "deepgait planning and control of quadrupedal gaits using deep reinforcement learning"
    - "norml no reward meta learning"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "learning agile and dynamic motor skills for legged robots"

Connected through hyperedge HE-2 (induced by source paper "deepracer educational autonomous racing platform for experimentation with sim2real reinforcement learning"):
  with members:
    - "deep drone racing from simulation to reality with domain randomization"
    - "f1 10 an open source autonomous cyber physical platform"
    - "mushr a low cost open source robotic racecar for education and research"
    - "quasi direct drive for low cost compliant robotic manipulation"
    - "self driving scale car trained by deep reinforcement learning"
    - "an online learning approach to model predictive control"
    - "learning agile and dynamic motor skills for legged robots"
    - "demonstration guided deep reinforcement learning of control policies for dexterous human robot interaction"

Connected through hyperedge HE-3 (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"
    - "policies modulating trajectory generators"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning latent dynamics for planning from pixels"
    - "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-4 (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"
    - "using deep reinforcement learning to learn high level policies on the atrias biped"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "feedback control of a cassie bipedal robot walking standing and riding a segway"
    - "neural probabilistic motor primitives for humanoid control"
    - "active deep q learning with demonstration"

Connected through hyperedge HE-5 (induced by source paper "learning agile and dynamic motor skills for legged robots"):
  with members:
    - "learning symmetric and low energy locomotion"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "sim to real learning agile locomotion for quadruped robots"
    - "control of a quadrotor with reinforcement learning"
    - "fast trajectory optimization for legged robots using vertex based zmp constraints"
    - "an efficient optimal planning and control framework for quadrupedal locomotion"
    - "learning locomotion skills using deeprl does the choice of action space matter"
    - "emergence of locomotion behaviours in rich environments"

Connected through hyperedge HE-6 (induced by source paper "a learnable safety measure"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "real time control for autonomous racing based on viability theory"
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "bayesian optimization using domain knowledge on the atrias biped"
    - "hamilton jacobi reachability a brief overview and recent advances"
    - "safe exploration in finite markov decision processes with gaussian processes"
    - "safe controller optimization for quadrotors with gaussian processes"
    - "safe exploration in markov decision processes"

Connected through hyperedge HE-7 (induced by source paper "deep reinforcement learning based robot arm manipulation with efficient training data through simulation"):
  with members:
    - "hindsight experience replay"
    - "a deeper look at experience replay"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "prioritized experience replay"
    - "continuous control with deep reinforcement learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "online trajectory planning through combined trajectory optimization and function approximation application to the exoskeleton atalante"):
  with members:
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "the expressive power of neural networks a view from the width"
    - "a reduction of imitation learning and structured prediction to no regret online learning"
    - "search based structured prediction"

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)