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 "stacnas towards stable and consistent differentiable neural architecture search"):
  Abstract: Differentiable Neural Architecture Search algorithms such as DARTS have attracted much attention due to the low search cost and competitive accuracy. However, it has been observed that DARTS can be unstable, especially when applied to new problems. One cause of the instability is the difficulty of two-level optimization. In addition, we identify two other causes: (1) Multicollinearity of correlated/similar operations leads to unpredictable change of the architecture parameters during search; (2) The optimization complexity gap between the proxy search stage and the final training leads to suboptimal architectures. Based on these findings, we propose a two-stage grouped variable pruning algorithm using one-level optimization. In the first stage, the best group is activated, and in the second stage, the best operation in the activated group is selected. Extensive experiments verify the superiority of the proposed method both for accuracy and for stability. For the DARTS search space, the proposed strategy obtains state-of-the-art accuracies on CIFAR-10, CIFAR-100 and ImageNet. Code is available at https://github.com/susan0199/stacnas.

Includes members:
  Member "random search and reproducibility for neural architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "automl zero evolving machine learning algorithms from scratch")
  Member "pc darts partial channel connections for memory efficient differentiable architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nas count counting by density with neural architecture search")
  Member "single path nas designing hardware efficient convnets in less than 4 hours":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards efficient model compression via learned global ranking")
  Member "understanding and robustifying differentiable architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mixpath a unified approach for one shot neural architecture search")
  Member "xnas neural architecture search with expert advice":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cifar 10 image classification using feature ensembles")
  Member "progressive differentiable architecture search bridging the depth gap between search and evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bats binary architecture search")
  Member "darts improved differentiable architecture search with early stopping":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "nas count counting by density with neural architecture search")
  Member "fairnas rethinking evaluation fairness of weight sharing neural architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mixpath a unified approach for one shot neural architecture search")

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)