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 "unsupervised deep feature transfer for low resolution image classification"):
  Abstract: In this paper, we propose a simple while effective unsupervised deep feature transfer algorithm for low resolution image classification. No fine-tuning on convenet filters is required in our method. We use pre-trained convenet to extract features for both high- and low-resolution images, and then feed them into a two-layer feature transfer network for knowledge transfer. A SVM classifier is learned directly using these transferred low resolution features. Our network can be embedded into the state-of-the-art deep neural networks as a plug-in feature enhancement module. It preserves data structures in feature space for high resolution images, and transfers the distinguishing features from a well-structured source domain (high resolution features space) to a not well-organized target domain (low resolution features space). Extensive experiments on VOC2007 test set show that the proposed method achieves significant improvements over the baseline of using feature extraction.

Includes members:
  Member "stacked wasserstein autoencoder":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adaptively denoising proposal collection for weakly supervised object localization")
  Member "adaptively denoising proposal collection for weakly supervised object localization":
    also connected through other hyperedges:
      (none)
  Member "adversarially approximated autoencoder for image generation and manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "self orthogonality module a network architecture plug in for learning orthogonal filters")
  Member "boosting occluded image classification via subspace decomposition based estimation of deep features":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self orthogonality module a network architecture plug in for learning orthogonal filters")
  Member "mdcn multi scale deep inception convolutional neural networks for efficient object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "self orthogonality module a network architecture plug in for learning orthogonal filters")
  Member "deep clustering for unsupervised learning of visual features":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning representations by predicting bags of visual words")
  Member "learning depth from single images with deep neural network embedding focal length":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "self orthogonality module a network architecture plug in for learning orthogonal filters")
  Member "invariant information clustering for unsupervised image classification and segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a comprehensive approach to unsupervised embedding learning based on and algorithm")

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)