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 "differentiable fixed point iteration layer"):
  Abstract: Recently, several studies proposed methods to utilize some restricted classes of optimization problems as layers of deep neural networks. However, these methods are still in their infancy and require special treatments, i.e., analyzing the KKT condition, etc., for deriving the backpropagation formula. Instead, in this paper, we propose a method to utilize fixed-point iteration (FPI), a generalization of many types of numerical algorithms, as a network layer. We show that the derivative of an FPI layer depends only on the fixed point, and then we present a method to calculate it efficiently using another FPI which we call the backward FPI. The proposed method can be easily implemented based on the autograd functionalities in existing deep learning tools. Since FPI covers vast different types of numerical algorithms in machine learning and other fields, it has a lot of potential applications. In the experiments, the differentiable FPI layer is applied to two scenarios, i.e., gradient descent iterations for differentiable optimization problems and FPI with arbitrary neural network modules, of which the results demonstrate the simplicity and the effectiveness.

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 "differentiable convex optimization layers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lossless compression of deep neural networks")
  Member "anderson acceleration for geometry optimization and physics simulation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient cloth simulation using miniature cloth and upscaling deep neural networks")
  Member "acquisition of localization confidence for accurate object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "revisiting the sibling head in object detector")
  Member "deep value networks learn to evaluate and iteratively refine structured outputs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial structure matching for structured prediction tasks")
  Member "optnet differentiable optimization as a layer in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lossless compression of deep neural networks")
  Member "input convex neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep multi agent reinforcement learning for decentralized continuous cooperative control")
  Member "structured prediction energy networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the differentiable cross entropy method")

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)