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 "mpc net a first principles guided policy search"):
  Abstract: We present an Imitation Learning approach for the control of dynamical systems with a known model. Our policy search method is guided by solutions from Model Predictive Control (MPC). Contrary to approaches that minimize a distance metric between the guiding demonstrations and the learned policy, our loss function corresponds to the minimization of the control Hamiltonian, which derives from the principle of optimality. Our algorithm, therefore, directly attempts to solve the HJB optimality equation with a parameterized class of control laws. The loss function's explicit encoding of physical constraints manifests in an improved constraint satisfaction metric of the learned controller. We train a mixture-of-expert neural network architecture for controlling a quadrupedal robot and show that this policy structure is well suited for such multimodal systems. The learned policy can successfully stabilize different gaits on the real walking robot from less than 10 min of demonstration data.

Includes members:
  Member "feedback mpc for torque controlled legged robots":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "whole body mpc for a dynamically stable mobile manipulator")
  Member "data efficient reinforcement learning for legged robots":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to walk in the real world with minimal human effort")
  Member "iterative reinforcement learning based design of dynamic locomotion skills for cassie":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to manipulate object collections using grounded state representations")
  Member "on the convergence of adam and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "learning agile and dynamic motor skills for legged robots":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rapidly adaptable legged robots via evolutionary meta learning")
  Member "policies modulating trajectory generators":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multiplicative controller fusion a hybrid navigation strategy for deployment in unknown environments")
  Member "an algorithmic perspective on imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning visuomotor policies for aerial navigation using cross modal representations")
  Member "sim to real learning agile locomotion for quadruped robots":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning to fly via deep model based 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)