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: "a taxonomy of deep convolutional neural nets for computer vision"
  Abstract: Traditional architectures for solving computer vision problems and the degree of success they enjoyed have been heavily reliant on hand-crafted features. However, of late, deep learning techniques have offered a compelling alternative -- that of automatically learning problem-specific features. With this new paradigm, every problem in computer vision is now being re-examined from a deep learning perspective. Therefore, it has become important to understand what kind of deep networks are suitable for a given problem. Although general surveys of this fast-moving paradigm (i.e. deep-networks) exist, a survey specific to computer vision is missing. We specifically consider one form of deep networks widely used in computer vision - convolutional neural networks (CNNs). We start with "AlexNet'' as our base CNN and then examine the broad variations proposed over time to suit different applications. We hope that our recipe-style survey will serve as a guide, particularly for novice practitioners intending to use deep-learning techniques for computer vision.

Connected through hyperedge HE-1 (induced by source paper "pi net a deep learning approach to extract topological persistence images"):
  with members:
    - "topology of learning in artificial neural networks"
    - "neural persistence a complexity measure for deep neural networks using algebraic topology"
    - "exposition and interpretation of the topology of neural networks"
    - "perturbation robust representations of topological persistence diagrams"
    - "quasi periodicity quantification in video data using topology"
    - "deep learning with topological signatures"
    - "persistent homology of attractors for action recognition"
    - "dense optical flow prediction from a static image"

Connected through hyperedge HE-2 (induced by source paper "spatio semantic convnet based visual place recognition"):
  with members:
    - "a holistic visual place recognition approach using lightweight cnns for severe viewpoint and appearance changes"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "convolutional neural network architecture for geometric matching"
    - "deep learning features at scale for visual place recognition"
    - "bags of local convolutional features for scalable instance search"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "convolutional neural network based image representation for visual loop closure detection"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-3 (induced by source paper "neurostream scalable and energy efficient deep learning with smart memory cubes"):
  with members:
    - "origami a 803 gop s w convolutional network accelerator"
    - "a near threshold risc v core with dsp extensions for scalable iot endpoint devices"
    - "deep neural networks are robust to weight binarization and other non linear distortions"
    - "identity mappings in deep residual networks"
    - "eie efficient inference engine on compressed deep neural network"
    - "acoustic scene classification using convolutional neural network and multiple width frequency delta data augmentation"
    - "fathom reference workloads for modern deep learning methods"
    - "recent advances in convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"):
  with members:
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "improved training of wasserstein gans"
    - "deep de aliasing for fast compressive sensing mri"
    - "medical image synthesis with context aware generative adversarial networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "neural photo editing with introspective adversarial networks"
    - "nips 2016 tutorial generative adversarial networks"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-5 (induced by source paper "large scale audio visual video analytics platform for forensic investigations of terroristic attacks"):
  with members:
    - "one deep music representation to rule them all a comparative analysis of different representation learning strategies"
    - "multi temporal resolution convolutional neural networks for acoustic scene classification"
    - "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging"
    - "kapre on gpu audio preprocessing layers for a quick implementation of deep neural network models with keras"
    - "simple online and realtime tracking with a deep association metric"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "sound event detection in multichannel audio using spatial and harmonic features"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-6 (induced by source paper "deep learning for digital text analytics sentiment analysis"):
  with members:
    - "social media analysis based on semanticity of streaming and batch data"
    - "vector space model as cognitive space for text classification"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-7 (induced by source paper "3d sketching using multi view deep volumetric prediction"):
  with members:
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "octnetfusion learning depth fusion from data"
    - "deepsketch2face a deep learning based sketching system for 3d face and caricature modeling"
    - "hierarchical surface prediction for 3d object reconstruction"
    - "surfacenet an end to end 3d neural network for multiview stereopsis"
    - "3d shape reconstruction from sketches via multi view convolutional networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "interactive 3d modeling with a generative adversarial network"

Connected through hyperedge HE-8 (induced by source paper "towards cnn map representation and compression for camera relocalisation"):
  with members:
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "learning to see by moving"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "matconvnet convolutional neural networks for matlab"

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)