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 "learning active spine behaviors for dynamic and efficient locomotion in quadruped robots"):
  Abstract: In this work, we provide a simulation framework to perform systematic studies on the effects of spinal joint compliance and actuation on bounding performance of a 16-DOF quadruped spined robot Stoch 2. Fast quadrupedal locomotion with active spine is an extremely hard problem, and involves a complex coordination between the various degrees of freedom. Therefore, past attempts at addressing this problem have not seen much success. Deep-Reinforcement Learning seems to be a promising approach, after its recent success in a variety of robot platforms, and the goal of this paper is to use this approach to realize the aforementioned behaviors. With this learning framework, the robot reached a bounding speed of 2.1 m/s with a maximum Froude number of 2. Simulation results also show that use of active spine, indeed, increased the stride length, improved the cost of transport, and also reduced the natural frequency to more realistic values.

Includes members:
  Member "iterative reinforcement learning based design of dynamic locomotion skills for cassie":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to manipulate object collections using grounded state representations")
  Member "design development and experimental realization of a quadrupedal research platform stoch":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gait library synthesis for quadruped robots via augmented random search")
  Member "sim to real learning agile locomotion for quadruped robots":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to fly via deep model based reinforcement learning")
  Member "oncilla robot a versatile open source quadruped research robot with compliant pantograph legs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an open torque controlled modular robot architecture for legged locomotion research")
  Member "soft actor critic algorithms and applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ready policy one world building through active learning")
  Member "realizing learned quadruped locomotion behaviors through kinematic motion primitives":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rapidly adaptable legged robots via evolutionary meta learning")
  Member "proximal policy optimization algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "tensorflow agents efficient batched reinforcement learning in tensorflow":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "surreal system fully integrated stack for distributed deep reinforcement learning")

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)