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 "sub architecture ensemble pruning in neural architecture search"):
  Abstract: Neural architecture search (NAS) is gaining more and more attention in recent years due to its flexibility and the remarkable capability of reducing the burden of neural network design. To achieve better performance, however, the searching process usually costs massive computation, which might not be affordable to researchers and practitioners. While recent attempts have employed ensemble learning methods to mitigate the enormous computation, an essential characteristic of diversity in ensemble methods is missed out, causing more similar sub-architectures to be gathered and potential redundancy in the final ensemble architecture. To bridge this gap, we propose a pruning method for NAS ensembles, named as ''Sub-Architecture Ensemble Pruning in Neural Architecture Search (SAEP).'' It targets to utilize diversity and achieve sub-ensemble architectures in a smaller size with comparable performance to the unpruned ensemble architectures. Three possible solutions are proposed to decide which subarchitectures should be pruned during the searching process. Experimental results demonstrate the effectiveness of the proposed method in largely reducing the size of ensemble architectures while maintaining the final performance. Moreover, distinct deeper architectures could be discovered if the searched sub-architectures are not diverse enough.

Includes members:
  Member "improving neural architecture search image classifiers via ensemble learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards learning of filter level heterogeneous compression of convolutional neural networks")
  Member "a survey on neural architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "to share or not to share a comprehensive appraisal of weight sharing")
  Member "towards automated deep learning efficient joint neural architecture and hyperparameter search":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "automl zero evolving machine learning algorithms from scratch")
  Member "regularized evolution for image classifier architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "searching for winograd aware quantized networks")
  Member "darts differentiable architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "neural architecture search with bayesian optimisation and optimal transport":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "real time federated evolutionary neural architecture search")
  Member "blockqnn efficient block wise neural network architecture generation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "automated design of error resilient and hardware efficient deep neural networks")
  Member "efficient neural architecture search via parameter sharing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "meta learning curiosity algorithms")

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)