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 "lossless compression of deep neural networks"):
  Abstract: Deep neural networks have been successful in many predictive modeling tasks, such as image and language recognition, where large neural networks are often used to obtain good accuracy. Consequently, it is challenging to deploy these networks under limited computational resources, such as in mobile devices. In this work, we introduce an algorithm that removes units and layers of a neural network while not changing the output that is produced, which thus implies a lossless compression. This algorithm, which we denote as LEO (Lossless Expressiveness Optimization), relies on Mixed-Integer Linear Programming (MILP) to identify Rectifier Linear Units (ReLUs) with linear behavior over the input domain. By using L1 regularization to induce such behavior, we can benefit from training over a larger architecture than we would later use in the environment where the trained neural network is deployed.

Includes members:
  Member "mipaal mixed integer program as a layer":
    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 "caql continuous action q learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep rbf value functions for continuous control")
  Member "function approximation by deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hp vpinns variational physics informed neural networks with domain decomposition")
  Member "equivalent and approximate transformations of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "caql continuous action q learning")
  Member "differentiable convex optimization layers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lp sparsemap differentiable relaxed optimization for sparse structured prediction")
  Member "accelerating primal solution findings for mixed integer programs based on solution prediction":
    also connected through other hyperedges:
      (none)
  Member "reinforcement learning for integer programming learning to cut":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimal solution predictions for mixed integer programs")
  Member "machine learning for combinatorial optimization a methodological tour d horizon":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "benchmarking graph 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)