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 "scarlet nas bridging the gap between stability and scalability in weight sharing neural architecture search"):
  Abstract: To discover compact models of great power is the goal of neural architecture search. Previous one-shot approaches are limited by fixed-depth search spaces. Simply paralleling skip connections with other choices can make depths variable. Unfortunately, it creates a large range of perturbation for supernet training, which makes it difficult to evaluate models. In this paper, we unveil its root cause under single-path settings and tackle the problem by imposing an equivariant learnable stabilizer on each skip connection. It has threefold benefits: improved convergence, more reliable evaluation, and retained equivalence. The third benefit is of the uttermost importance for scalability. As appending stabilizers to a model doesn't change its representational capacity, we can now evaluate the stabilized counterpart as an identical proxy. With an evolutionary search backend that treats the supernet as an evaluator, we derive a family of state-of-the-art architectures, the SCARLET (SCAlable supeRnet with Learnable Equivariant sTablizer)series, at a tremendously reduced cost compared with EfficientNet.The models and evaluation code are released online this https URL .

Includes members:
  Member "single path nas designing hardware efficient convnets in less than 4 hours":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards efficient model compression via learned global ranking")
  Member "searching for a robust neural architecture in four gpu hours":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning architectures for binary networks")
  Member "understanding and robustifying differentiable architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mixpath a unified approach for one shot neural architecture search")
  Member "searching for mobilenetv3":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "single path one shot neural architecture search with uniform sampling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "moga searching beyond mobilenetv3":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "blockwisely supervised neural architecture search with knowledge distillation")
  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")
  Member "evaluating the search phase of neural architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "semi supervised 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)