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 "randomnet towards fully automatic neural architecture design for multimodal learning"):
  Abstract: Almost all neural architecture search methods are evaluated in terms of performance (i.e. test accuracy) of the model structures that it finds. Should it be the only metric for a good autoML approach? To examine aspects beyond performance, we propose a set of criteria aimed at evaluating the core of autoML problem: the amount of human intervention required to deploy these methods into real world scenarios. Based on our proposed evaluation checklist, we study the effectiveness of a random search strategy for fully automated multimodal neural architecture search. Compared to traditional methods that rely on manually crafted feature extractors, our method selects each modality from a large search space with minimal human supervision. We show that our proposed random search strategy performs close to the state of the art on the AV-MNIST dataset while meeting the desirable characteristics for a fully automated design process.

Includes members:
  Member "mfas multimodal fusion architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural inheritance relation guided one shot layer assignment search")
  Member "best practices for scientific research on neural architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nas bench 1shot1 benchmarking and dissecting one shot neural architecture search")
  Member "streaming end to end speech recognition for mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a simplified fully quantized transformer for end to end speech recognition")
  Member "centralnet a multilayer approach for multimodal fusion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mmtm multimodal transfer module for cnn fusion")
  Member "efficient neural architecture search via parameter sharing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "meta learning curiosity algorithms")
  Member "learning transferable architectures for scalable image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "meta learning curiosity algorithms")
  Member "efficient architecture search by network transformation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "nas count counting by density with neural architecture search")
  Member "truly multi modal youtube 8m video classification with video audio and text":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning to separate object sounds by watching unlabeled video")

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)