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: "hierarchical reinforcement learning for quadruped locomotion"
  Abstract: Legged locomotion is a challenging task for learning algorithms, especially when the task requires a diverse set of primitive behaviors. To solve these problems, we introduce a hierarchical framework to automatically decompose complex locomotion tasks. A high-level policy issues commands in a latent space and also selects for how long the low-level policy will execute the latent command. Concurrently, the low-level policy uses the latent command and only the robot's on-board sensors to control the robot's actuators. Our approach allows the high-level policy to run at a lower frequency than the low-level one. We test our framework on a path-following task for a dynamic quadruped robot and we show that steering behaviors automatically emerge in the latent command space as low-level skills are needed for this task. We then show efficient adaptation of the trained policy to a different task by transfer of the trained low-level policy. Finally, we validate the policies on a real quadruped robot. To the best of our knowledge, this is the first application of end-to-end hierarchical learning to a real robotic locomotion task.

Connected through hyperedge HE-1 (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"
    - "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"
    - "learning dexterous in hand manipulation"

Connected through hyperedge HE-2 (induced by source paper "multi agent manipulation via locomotion using hierarchical sim2real"):
  with members:
    - "end to end robotic reinforcement learning without reward engineering"
    - "active domain randomization"
    - "bayessim adaptive domain randomization via probabilistic inference for robotics simulators"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "sim to real learning agile locomotion for quadruped robots"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"

Connected through hyperedge HE-3 (induced by source paper "gait library synthesis for quadruped robots via augmented random search"):
  with members:
    - "iterative reinforcement learning based design of dynamic locomotion skills for cassie"
    - "design development and experimental realization of a quadrupedal research platform stoch"
    - "learning agile and dynamic motor skills for legged robots"
    - "policies modulating trajectory generators"
    - "sim to real learning agile locomotion for quadruped robots"
    - "realizing learned quadruped locomotion behaviors through kinematic motion primitives"
    - "learning locomotion skills using deeprl does the choice of action space matter"
    - "proximal policy optimization algorithms"

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)