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 "darts differentiable architecture search"):
  Abstract: This paper addresses the scalability challenge of architecture search by formulating the task in a differentiable manner. Unlike conventional approaches of applying evolution or reinforcement learning over a discrete and non-differentiable search space, our method is based on the continuous relaxation of the architecture representation, allowing efficient search of the architecture using gradient descent. Extensive experiments on CIFAR-10, ImageNet, Penn Treebank and WikiText-2 show that our algorithm excels in discovering high-performance convolutional architectures for image classification and recurrent architectures for language modeling, while being orders of magnitude faster than state-of-the-art non-differentiable techniques. Our implementation has been made publicly available to facilitate further research on efficient architecture search algorithms.

Includes members:
  Member "regularized evolution for image classifier architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "searching for winograd aware quantized networks")
  Member "connectivity learning in multi branch networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "assemblenet searching for multi stream neural connectivity in video architectures")
  Member "neural architecture search with bayesian optimisation and optimal transport":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "real time federated evolutionary neural architecture search")
  Member "efficient neural architecture search via parameter sharing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "meta learning curiosity algorithms")
  Member "shufflenet an extremely efficient convolutional neural network for mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "post training piecewise linear quantization for deep neural networks")
  Member "progressive neural architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "learning transferable architectures for scalable image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "meta learning curiosity algorithms")
  Member "breaking the softmax bottleneck a high rank rnn language model":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relational state space model for stochastic multi object systems")

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)