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 "semi supervised neural architecture search"):
  Abstract: Neural architecture search (NAS) relies on a good controller to generate better architectures or predict the accuracy of given architectures. However, training the controller requires both abundant and high-quality pairs of architectures and their accuracy, while it is costly to evaluate an architecture and obtain its accuracy. In this paper, we propose \emph{SemiNAS}, a semi-supervised NAS approach that leverages numerous unlabeled architectures~(without evaluation and thus nearly no cost) to improve the controller. Specifically, SemiNAS 1) trains an initial controller with a small set of architecture-accuracy data pairs; 2) uses the trained controller to predict the accuracy of large amount of architectures~(without evaluation); and 3) adds the generated data pairs to the original data to further improve the controller. SemiNAS has two advantages: 1) It reduces the computational cost under the same accuracy guarantee. 2) It achieves higher accuracy under the same computational cost. On NASBench-101 benchmark dataset, it discovers a top 0.01% architecture after evaluating roughly 300 architectures, with only 1/7 computational cost compared with regularized evolution and gradient-based methods. On ImageNet, it achieves 24.2% top-1 error rate (under the mobile setting) using 4 GPU-days for search. We further apply it to LJSpeech text to speech task and it achieves 97% intelligibility rate in the low-resource setting and 15% test error rate in the robustness setting, with 9%, 7% improvements over the baseline respectively. Our code is available at this https URL.

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 "neural predictor for neural architecture search":
    also connected through other hyperedges:
      (none)
  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 "fastspeech fast robust and controllable text to speech":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "waveflow a compact flow based model for raw audio")
  Member "the evolved transformer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "automl zero evolving machine learning algorithms from scratch")
  Member "single path one shot neural architecture search with uniform sampling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "progressive differentiable architecture search bridging the depth gap between search and evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bats binary 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)