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: "robust minutiae extractor integrating deep networks and fingerprint domain knowledge"
  Abstract: We propose a fully automatic minutiae extractor, called MinutiaeNet, based on deep neural networks with compact feature representation for fast comparison of minutiae sets. Specifically, first a network, called CoarseNet, estimates the minutiae score map and minutiae orientation based on convolutional neural network and fingerprint domain knowledge (enhanced image, orientation field, and segmentation map). Subsequently, another network, called FineNet, refines the candidate minutiae locations based on score map. We demonstrate the effectiveness of using the fingerprint domain knowledge together with the deep networks. Experimental results on both latent (NIST SD27) and plain (FVC 2004) public domain fingerprint datasets provide comprehensive empirical support for the merits of our method. Further, our method finds minutiae sets that are better in terms of precision and recall in comparison with state-of-the-art on these two datasets. Given the lack of annotated fingerprint datasets with minutiae ground truth, the proposed approach to robust minutiae detection will be useful to train network-based fingerprint matching algorithms as well as for evaluating fingerprint individuality at scale. MinutiaeNet is implemented in Tensorflow: this https URL

Connected through hyperedge HE-1 (induced by source paper "universal material translator towards spoof fingerprint generalization"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "fingerprint presentation attack detection generalization and efficiency"
    - "raspireader open source fingerprint reader"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "demystifying neural style transfer"
    - "texturegan controlling deep image synthesis with texture patches"
    - "review of the fingerprint liveness detection livdet competition series 2009 to 2015"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"

Connected through hyperedge HE-2 (induced by source paper "fingerprints fixed length representation via deep networks and domain knowledge"):
  with members:
    - "automatic cropping fingermarks latent fingerprint segmentation"
    - "multi task convolutional neural network for pose invariant face recognition"
    - "automated latent fingerprint recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "fingernet an unified deep network for fingerprint minutiae extraction"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "spatial transformer networks"
    - "multi task cnn model for attribute prediction"

Connected through hyperedge HE-3 (induced by source paper "learning a fixed length fingerprint representation"):
  with members:
    - "automatic latent fingerprint segmentation"
    - "multi task convolutional neural network for pose invariant face recognition"
    - "end to end latent fingerprint search"
    - "secure face matching using fully homomorphic encryption"
    - "automated latent fingerprint recognition"
    - "fingernet an unified deep network for fingerprint minutiae extraction"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "spatial transformer networks"

Connected through hyperedge HE-4 (induced by source paper "u finger multi scale dilated convolutional network for fingerprint image denoising and inpainting"):
  with members:
    - "enhance visual recognition under adverse conditions via deep networks"
    - "improved techniques for learning to dehaze and beyond a collective study"
    - "when image denoising meets high level vision tasks a deep learning approach"
    - "fingernet an unified deep network for fingerprint minutiae extraction"
    - "studying very low resolution recognition using deep networks"
    - "multi scale context aggregation by dilated convolutions"
    - "learning super resolution jointly from external and internal examples"

Connected through hyperedge HE-5 (induced by source paper "end to end pore extraction and matching in latent fingerprints going beyond minutiae"):
  with members:
    - "fingerprints fixed length representation via deep networks and domain knowledge"
    - "infant prints fingerprints for reducing infant mortality"
    - "automatic latent fingerprint segmentation"
    - "end to end latent fingerprint search"
    - "automatic dataset annotation to learn cnn pore description for fingerprint recognition"

Connected through hyperedge HE-6 (induced by source paper "fpd m net fingerprint image denoising and inpainting using m net based convolutional neural networks"):
  with members:
    - "generative convolutional networks for latent fingerprint reconstruction"
    - "fingernet an unified deep network for fingerprint minutiae extraction"
    - "u net convolutional networks for biomedical image segmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-7 (induced by source paper "end to end latent fingerprint search"):
  with members:
    - "automatic latent fingerprint segmentation"
    - "latent fingerprint recognition role of texture template"
    - "on the intrinsic dimensionality of face representation"
    - "automated latent fingerprint recognition"
    - "fingernet an unified deep network for fingerprint minutiae extraction"
    - "latent fingerprint minutia extraction using fully convolutional network"
    - "deepfool a simple and accurate method to fool deep neural networks"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"

Connected through hyperedge HE-8 (induced by source paper "automatic cropping fingermarks latent fingerprint segmentation"):
  with members:
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "fully convolutional networks for semantic segmentation"

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)