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: "learning symmetric and low energy locomotion"
  Abstract: We introduce a new loss term and a physical assistance curriculum to deep reinforcement learning algorithm and demonstrate learning symmetric and low-energy locomotion for a variety of characters from scratch.

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 "sim to real transfer for biped locomotion"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "learning dexterous in hand manipulation"
    - "policy transfer with strategy optimization"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "sim to real learning agile locomotion for quadruped robots"
    - "hardware conditioned policies for multi robot transfer learning"
    - "closing the sim to real loop adapting simulation randomization with real world experience"

Connected through hyperedge HE-3 (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"
    - "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"
    - "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-4 (induced by source paper "learning agile and dynamic motor skills for legged robots"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "sim to real learning agile locomotion for quadruped robots"
    - "feedback control for cassie with deep reinforcement learning"
    - "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-5 (induced by source paper "visualizing movement control optimization landscapes"):
  with members:
    - "deepmind control suite"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "unity a general platform for intelligent agents"
    - "visualizing the loss landscape of neural nets"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "sharp minima can generalize for deep nets"
    - "learning locomotion skills using deeprl does the choice of action space matter"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-6 (induced by source paper "data efficient and safe learning for locomotion via simplified model"):
  with members:
    - "dynamic locomotion for passive ankle biped robots and humanoids using whole body locomotion control"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks"
    - "model ensemble trust region policy optimization"
    - "stair climbing stabilization of the hrp 4 humanoid robot using whole body admittance control"
    - "fast kinodynamic bipedal locomotion planning with moving obstacles"
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"

Connected through hyperedge HE-7 (induced by source paper "deep learned path planning via randomized reward linked goals and potential space applications"):
  with members:
    - "an investigation of model free planning"
    - "hierarchical visuomotor control of humanoids"
    - "fast approximate clearance evaluation for kinematically constrained articulated suspension systems"
    - "sim to real learning agile locomotion for quadruped robots"
    - "addressing function approximation error in actor critic methods"
    - "learning to walk via deep reinforcement learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "emergence of locomotion behaviours in rich environments"

Connected through hyperedge HE-8 (induced by source paper "self imitation learning of locomotion movements through termination curriculum"):
  with members:
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "ppo cma proximal policy optimization with covariance matrix adaptation"
    - "sfv reinforcement learning of physical skills from videos"
    - "intelligent middle level game control"
    - "maximum a posteriori policy optimisation"
    - "proximal policy optimization algorithms"
    - "trust region policy optimization"

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)