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: "data efficient reinforcement learning for legged robots"
  Abstract: We present a model-based framework for robot locomotion that achieves walking based on only 4.5 minutes (45,000 control steps) of data collected on a quadruped robot. To accurately model the robot's dynamics over a long horizon, we introduce a loss function that tracks the model's prediction over multiple timesteps. We adapt model predictive control to account for planning latency, which allows the learned model to be used for real time control. Additionally, to ensure safe exploration during model learning, we embed prior knowledge of leg trajectories into the action space. The resulting system achieves fast and robust locomotion. Unlike model-free methods, which optimize for a particular task, our planner can use the same learned dynamics for various tasks, simply by changing the reward function. To the best of our knowledge, our approach is more than an order of magnitude more sample efficient than current model-free methods.

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"
    - "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"
    - "learning fast adaptation with meta strategy optimization"

Connected through hyperedge HE-2 (induced by source paper "ppmc rl training algorithm rough terrain intelligent robots through reinforcement learning"):
  with members:
    - "fast approximate clearance evaluation for rovers with articulated suspension systems"
    - "adaptive tensegrity locomotion on rough terrain via reinforcement learning"
    - "residual reinforcement learning for robot control"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "emergence of locomotion behaviours in rich environments"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"

Connected through hyperedge HE-3 (induced by source paper "mpc net a first principles guided policy search"):
  with members:
    - "feedback mpc for torque controlled 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"
    - "policies modulating trajectory generators"
    - "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-4 (induced by source paper "learning generalizable locomotion skills with hierarchical reinforcement learning"):
  with members:
    - "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"
    - "using deep reinforcement learning to learn high level policies on the atrias biped"
    - "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-5 (induced by source paper "autonomous control of a tendon driven robotic limb with elastic elements reveals that added elasticity can enhance learning"):
  with members:
    - "simple kinematic feedback enhances autonomous learning in bio inspired tendon driven systems"
    - "emergence of locomotion behaviours in rich environments"
    - "proximal policy optimization algorithms"
    - "continuous control with deep reinforcement learning"
    - "robots that can adapt like animals"

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.LG(Machine Learning)