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 "mixpath a unified approach for one shot neural architecture search"):
  Abstract: The expressiveness of search space is a key concern in neural architecture search (NAS). Previous approaches are mainly limited to searching for single-path networks. Incorporating multi-path search space with the current one-shot doctrine remains untackled. In this paper, we investigate the supernet behavior under multi-path's setting. We show that a trivial generalization from single-path to multi-path incurs severe feature inconsistency, which deteriorates both supernet training stability and model ranking ability. To remedy this degradation, we employ what we term as shadow batch normalizations (SBN) to catch changing statistics when activating different sets of paths. Extensive experiments on a common NAS benchmark, NAS-bench-101, show that SBN can boost ranking performance at neglectable cost. It breaks the Kendall Tau's record with a clear margin, reaching 0.597. Moreover, we take advantage of feature similarities on activated paths to largely reduce the number of needed SBNs. We call our method MixPath. When proxylessly searching on ImageNet, we obtain several lightweight models that outperform EfficientNet-B0 with fewer FLOPs, parameters and 300x fewer searching resources. Our code will be available this https URL .

Includes members:
  Member "fast accurate and lightweight super resolution with neural architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hierarchical neural architecture search for single image super resolution")
  Member "single path nas designing hardware efficient convnets in less than 4 hours":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards efficient model compression via learned global ranking")
  Member "scarlet nas bridging the gap between stability and scalability in weight sharing neural architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "blockwisely supervised neural architecture search with knowledge distillation")
  Member "searching for a robust neural architecture in four gpu hours":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning architectures for binary networks")
  Member "fair darts eliminating unfair advantages in differentiable architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stabilizing differentiable architecture search via perturbation based regularization")
  Member "understanding and robustifying differentiable architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stabilizing differentiable architecture search via perturbation based regularization")
  Member "the evolved transformer":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "automl zero evolving machine learning algorithms from scratch")
  Member "mixconv mixed depthwise convolutional kernels":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "udd an underwater open sea farm object detection dataset for underwater robot picking")

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)