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 "pontryagin differentiable programming an end to end learning and control framework"):
  Abstract: This paper develops a Pontryagin differentiable programming (PDP) methodology to establish a unified end-to-end learning framework, which solves a large class of learning and control tasks. The proposed PDP framework distinguishes itself from existing ones by two key techniques: first, by differentiating the Pontryagin's Maximum Principle, the PDP framework allows for an end-to-end learning of any parameterized system, even though differentiation with respect to an unknown objective function is not readily attainable; and second, based on control theory, the PDP framework incorporates both the forward and backward propagations by constructing two separate control systems, which are then efficiently solved using techniques in control domain. Three learning modes of the proposed PDP framework are investigated to address three types of learning problems: inverse optimization, system identification, and control/planing, respectively. Effectiveness of this framework in each learning mode has been validated in the context of pendulum systems.

Includes members:
  Member "deep learning theory review an optimal control and dynamical systems perspective":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "differential dynamic programming neural optimizer")
  Member "mpc inspired neural network policies for sequential decision making":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the differentiable cross entropy method")
  Member "plan online learn offline efficient learning and exploration via model based control":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural lyapunov model predictive control")
  Member "an optimal control approach to deep learning and applications to discrete weight neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "differential dynamic programming neural optimizer")
  Member "maximum principle based algorithms for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "differential dynamic programming neural optimizer")
  Member "path integral networks end to end differentiable optimal control":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "planet of the bayesians reconsidering and improving deep planning network by incorporating bayesian inference")
  Member "deep learning approximation for stochastic control problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a machine learning framework for solving high dimensional mean field game and mean field control problems")
  Member "value iteration networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "plannable approximations to mdp homomorphisms equivariance under actions")

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.LG(Machine Learning)