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 "one shot neural architecture search via compressive sensing"):
  Abstract: Neural architecture search (NAS), or automated design of neural network models, remains a very challenging meta-learning problem. Several recent works (called "one-shot" approaches) have focused on dramatically reducing NAS running time by leveraging proxy models that still provide architectures with competitive performance. In our work, we propose a new meta-learning algorithm that we call CoNAS, or Compressive sensing-based Neural Architecture Search. Our approach merges ideas from one-shot approaches with iterative techniques for learning low-degree sparse Boolean polynomial functions. We validate our approach on several standard test datasets, discover novel architectures hitherto unreported, and achieve competitive (or better) results in both performance and search time compared to existing NAS approaches. Further, we support our algorithm with a theoretical analysis, providing upper bounds on the number of measurements needed to perform reliable meta-learning; to our knowledge, these analysis tools are novel to the NAS literature and may be of independent interest.

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 "exploring randomly wired neural networks for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "automl zero evolving machine learning algorithms from scratch")
  Member "learnable embedding space for efficient neural architecture compression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "nat neural architecture transformer for accurate and compact architectures")
  Member "evaluating the search phase of neural architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semi supervised neural architecture search")
  Member "massively parallel hyperparameter tuning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hyper parameter optimization a review of algorithms and applications")
  Member "graph hypernetworks for neural architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning architectures for binary networks")
  Member "regularized evolution for image classifier architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "searching for winograd aware quantized networks")
  Member "proxylessnas direct neural architecture search on target task and hardware":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gan compression efficient architectures for interactive conditional gans")

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)