human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "fair darts eliminating unfair advantages in differentiable architecture search"):
  with members:
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "one shot neural architecture search via self evaluated template network"
    - "searching for a robust neural architecture in four gpu hours"
    - "understanding and robustifying differentiable architecture search"
    - "searching for mobilenetv3"
    - "mixconv mixed depthwise convolutional kernels"
    - "xnas neural architecture search with expert advice"

Connected through hyperedge HE-2 (induced by source paper "nas evaluation is frustratingly hard"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "sharpdarts faster and more accurate differentiable architecture search"
    - "exploring randomly wired neural networks for image recognition"
    - "xnas neural architecture search with expert advice"
    - "evaluating the search phase of neural architecture search"
    - "manas multi agent neural architecture search"
    - "darts improved differentiable architecture search with early stopping"
    - "best practices for scientific research on neural architecture search"

Question: Please tell me which class the center node 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)