human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "self orthogonality module a network architecture plug in for learning orthogonal filters"):
  with members:
    - "orthogonal deep neural networks"
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "mdfn multi scale deep feature learning network for object detection"
    - "towards learning affine invariant representations via data efficient cnns"
    - "boosting occluded image classification via subspace decomposition based estimation of deep features"
    - "interpretable convolutional neural networks via feedforward design"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "regularization for deep learning a taxonomy"

Connected through hyperedge HE-2 (induced by source paper "mdfn multi scale deep feature learning network for object detection"):
  with members:
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "boosting occluded image classification via subspace decomposition based estimation of deep features"
    - "mdcn multi scale deep inception convolutional neural networks for efficient object detection"
    - "yolov3 an incremental improvement"
    - "a 2 nets double attention networks"
    - "learning depth from single images with deep neural network embedding focal length"
    - "non local neural networks"
    - "blitznet a real time deep network for scene understanding"

Connected through hyperedge HE-3 (induced by source paper "adaptively denoising proposal collection for weakly supervised object localization"):
  with members:
    - "stacked wasserstein autoencoder"
    - "direct visual inertial odometry with semi dense mapping"
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "towards learning affine invariant representations via data efficient cnns"
    - "mdcn multi scale deep inception convolutional neural networks for efficient object detection"
    - "learning depth from single images with deep neural network embedding focal length"
    - "soft nms improving object detection with one line of code"
    - "how hard can it be estimating the difficulty of visual search in an image"

Question: Please tell me which class the center node 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)