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 "implicit deep learning"):
  Abstract: We define a new class of "implicit" deep learning prediction rules that generalize the recursive rules of feedforward neural networks. These models are based on the solution of a fixed-point equation involving a single a vector of hidden features, which is thus only implicitly defined. The new framework greatly simplifies the notation of deep learning, and opens up new possibilities, in terms of novel architectures and algorithms, robustness analysis and design, interpretability, sparsity, and network architecture optimization.

Includes members:
  Member "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "it s not what machines can learn it s what we cannot teach")
  Member "deep equilibrium models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "accessing higher level representations in sequential transformers with feedback memory")
  Member "differentiable mpc for end to end planning and control":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "objective mismatch in model based reinforcement learning")
  Member "lifted proximal operator machines":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "differentiable linearized admm")
  Member "neural ordinary differential equations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stochastic latent residual video prediction")
  Member "fenchel lifted networks a lagrange relaxation of neural network training":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "effects of depth width and initialization a convergence analysis of layer wise training for deep linear neural networks")
  Member "lifted neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "effects of depth width and initialization a convergence analysis of layer wise training for deep linear neural networks")
  Member "optnet differentiable optimization as a layer in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "lossless compression of deep neural networks")

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)