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 "resnetx a more disordered and deeper network architecture"):
  Abstract: Designing efficient network structures has always been the core content of neural network research. #R##N#ResNet and its variants have proved to be efficient in architecture. #R##N#However, how to theoretically character the influence of network structure on performance is still vague. #R##N#With the help of techniques in complex networks, We here provide a natural yet efficient extension to ResNet by folding its backbone chain. #R##N#Our architecture has two structural features when being mapped to directed acyclic graphs: #R##N#First is a higher degree of the disorder compared with ResNet, which let ResNetX explore a larger number of feature maps with different sizes of receptive fields. #R##N#Second is a larger proportion of shorter paths compared to ResNet, which improves the direct flow of information through the entire network. #R##N#Our architecture exposes a new dimension, namely "fold depth", in addition to existing dimensions of depth, width, and cardinality. #R##N#Our architecture is a natural extension to ResNet, and can be integrated with existing state-of-the-art methods with little effort. Image classification results on CIFAR-10 and CIFAR-100 benchmarks suggested that our new network architecture performs better than ResNet.

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 "evaluating the search phase of neural architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "semi supervised neural architecture search")
  Member "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "searching for winograd aware quantized networks")
  Member "efficientnet rethinking model scaling for convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "big little net an efficient multi scale feature representation for visual and speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "slimconv reducing channel redundancy in convolutional neural networks by weights flipping")
  Member "efficient neural architecture search via parameter sharing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "meta learning curiosity algorithms")

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.CV(Computer Vision and Pattern Recognition)