human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "data efficient and safe learning for locomotion via simplified model"):
  Abstract: In this letter, we formulate a novel Markov Decision Process (MDP) for data efficient and safe learning for locomotion via a simplified model. In our previous studies on biped locomotion, we relied on a low-dimensional robot model, e.g., the Linear Inverted Pendulum Model (LIPM), commonly used in Walking Pattern Generators (WPG). However, employing low-level control cannot precisely track desired footstep locations due to the discrepancies between the real system and the simplified model. In this work, we propose an approach for mitigating this problem by complementing model-based policies with machine learning. We formulate an MDP process incorporating dynamic properties of robots, desired walking directions, and footstep features. We iteratively update the policy to determine footstep locations based on the previous MDP process aided by a deep reinforcement learning process. The policy of the proposed approach consists of a WPG and a parameterized stochastic policy. In addition, a Control Barrier Function (CBF) process applies corrections the above policy to prevent exploration of unsafe regions during learning. Our contributions include: 1) reduction of footstep tracking errors resulting from employing LIPM; 2) efficient exploration of the data driven process, and; 3) scalability of the procedure to any humanoid robot.

Includes members:
  Member "dynamic locomotion for passive ankle biped robots and humanoids using whole body locomotion control":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stance locomotion adaptation over soft terrain")
  Member "tossingbot learning to throw arbitrary objects with residual physics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to walk in the real world with minimal human effort")
  Member "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exploration exploitation in constrained mdps")
  Member "learning symmetric and low energy locomotion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to walk in the real world with minimal human effort")
  Member "model ensemble trust region policy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ready policy one world building through active learning")
  Member "stair climbing stabilization of the hrp 4 humanoid robot using whole body admittance control":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "biped stabilization by linear feedback of the variable height inverted pendulum model")
  Member "fast kinodynamic bipedal locomotion planning with moving obstacles":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "control of a high performance bipedal robot using viscoelastic liquid cooled actuators")
  Member "a general safety framework for learning based control in uncertain robotic systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning to walk in the real world with minimal human effort")

Question: Please tell me which class the source paper 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)