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 "svm and elm who wins object recognition with deep convolutional features from imagenet"):
  Abstract: Deep learning with a convolutional neural network (CNN) has been proved to be very effective in feature extraction and representation of images. For image classification problems, this work aim at finding which classifier is more competitive based on high-level deep features of images. In this paper, we have discussed the nearest neighbor, support vector machines and extreme learning machines for image classification under deep convolutional activation feature representation. Specifically, we adopt the benchmark object recognition dataset from multiple sources with domain bias for evaluating different classifiers. The deep features of the object dataset are obtained by a well-trained CNN with five convolutional layers and three fully-connected layers on the challenging ImageNet. Experiments demonstrate that the ELMs outperform SVMs in cross-domain recognition tasks. In particular, state-of-the-art results are obtained by kernel ELM which outperforms SVMs with about 4 % of the average accuracy. The Features and MATLAB codes in this paper are available in http://www.escience.cn/people/lei/index.html.

Includes members:
  Member "domain adaptation extreme learning machines for drift compensation in e nose systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transfer adaptation learning a decade survey")
  Member "naive deep face recognition touching the limit of lfw benchmark or not":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "person recognition in personal photo collections")
  Member "evolutionary cost sensitive extreme learning machine and subspace extension":
    also connected through other hyperedges:
      (none)
  Member "spatial pyramid pooling in deep convolutional networks for visual recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "cnn features off the shelf an astounding baseline for recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "decaf a deep convolutional activation feature for generic visual recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "crl class representative learning for image classification")
  Member "rich feature hierarchies for accurate object detection and semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "multi column deep neural networks for image classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "importance driven deep learning system testing")

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)