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: "fingernet an unified deep network for fingerprint minutiae extraction"
  Abstract: Minutiae extraction is of critical importance in automated fingerprint recognition. Previous works on rolled/slap fingerprints failed on latent fingerprints due to noisy ridge patterns and complex background noises. In this paper, we propose a new way to design deep convolutional network combining domain knowledge and the representation ability of deep learning. In terms of orientation estimation, segmentation, enhancement and minutiae extraction, several typical traditional methods performed well on rolled/slap fingerprints are transformed into convolutional manners and integrated as an unified plain network. We demonstrate that this pipeline is equivalent to a shallow network with fixed weights. The network is then expanded to enhance its representation ability and the weights are released to learn complex background variance from data, while preserving end-to-end differentiability. Experimental results on NIST SD27 latent database and FVC 2004 slap database demonstrate that the proposed algorithm outperforms the state-of-the-art minutiae extraction algorithms. Code is made publicly available at: this https URL

Connected through hyperedge HE-1 (induced by source paper "demographic bias in biometrics a survey on an emerging challenge"):
  with members:
    - "post comparison mitigation of demographic bias in face recognition using fair score normalization"
    - "50 years of test un fairness lessons for machine learning"
    - "diversity in faces"
    - "fairface face attribute dataset for balanced race gender and age"
    - "an experimental evaluation of covariates effects on unconstrained face verification"
    - "sensitivenets learning agnostic representations with application to face recognition"
    - "some research problems in biometrics the future beckons"
    - "mitigate bias in face recognition using skewness aware reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "neural fuzzy extractors a secure way to use artificial neural networks for biometric user authentication"):
  with members:
    - "fingerprints fixed length representation via deep networks and domain knowledge"
    - "in defense of the triplet loss for person re identification"
    - "deep image retrieval learning global representations for image search"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "facenet a unified embedding for face recognition and clustering"
    - "deep representations for iris face and fingerprint spoofing detection"
    - "very deep convolutional networks for large scale image recognition"
    - "low density lattice codes"

Connected through hyperedge HE-3 (induced by source paper "biometric recognition using deep learning a survey"):
  with members:
    - "p2sgrad refined gradients for optimizing deep face models"
    - "thirdeye triplet based iris recognition without normalization"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "adacos adaptively scaling cosine logits for effectively learning deep face representations"
    - "deepiris iris recognition using a deep learning approach"
    - "fingernet pushing the limits of fingerprint recognition using convolutional neural network"
    - "generating talking face landmarks from speech"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-4 (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"
    - "robust minutiae extractor integrating deep networks and fingerprint domain knowledge"
    - "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-5 (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"
    - "robust minutiae extractor integrating deep networks and fingerprint domain knowledge"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "spatial transformer networks"

Connected through hyperedge HE-6 (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"
    - "robust minutiae extractor integrating deep networks and fingerprint domain knowledge"
    - "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-7 (induced by source paper "fpd m net fingerprint image denoising and inpainting using m net based convolutional neural networks"):
  with members:
    - "robust minutiae extractor integrating deep networks and fingerprint domain knowledge"
    - "generative convolutional networks for latent fingerprint reconstruction"
    - "u net convolutional networks for biomedical image segmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-8 (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"
    - "robust minutiae extractor integrating deep networks and fingerprint domain knowledge"
    - "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"

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)