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: "writer independent feature learning for offline signature verification using deep convolutional neural networks"
  Abstract: Automatic Offline Handwritten Signature Verification has been researched over the last few decades from several perspectives, using insights from graphology, computer vision, signal processing, among others. In spite of the advancements on the field, building classifiers that can separate between genuine signatures and skilled forgeries (forgeries made targeting a particular signature) is still hard. We propose approaching the problem from a feature learning perspective. Our hypothesis is that, in the absence of a good model of the data generation process, it is better to learn the features from data, instead of using hand-crafted features that have no resemblance to the signature generation process. To this end, we use Deep Convolutional Neural Networks to learn features in a writer-independent format, and use this model to obtain a feature representation on another set of users, where we train writer-dependent classifiers. We tested our method in two datasets: GPDS-960 and Brazilian PUC-PR. Our experimental results show that the features learned in a subset of the users are discriminative for the other users, including across different datasets, reaching close to the state-of-the-art in the GPDS dataset, and improving the state-of-the-art in the Brazilian PUC-PR dataset.

Connected through hyperedge HE-1 (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-2 (induced by source paper "multi representational learning for offline signature verification using multi loss snapshot ensemble of cnns"):
  with members:
    - "active transfer learning for persian offline signature verification"
    - "learning representations from persian handwriting for offline signature verification a deep transfer learning approach"
    - "a writer independent approach for offline signature verification using deep convolutional neural networks features"
    - "fixed sized representation learning from offline handwritten signatures of different sizes"
    - "using mode connectivity for loss landscape analysis"
    - "a comprehensive study of sparse representation techniques for offline signature verification"
    - "offline handwritten signature verification literature review"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-3 (induced by source paper "meta learning for fast classifier adaptation to new users of signature verification systems"):
  with members:
    - "learning deep features for one class classification"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "how to train your maml"
    - "a writer independent approach for offline signature verification using deep convolutional neural networks features"
    - "fixed sized representation learning from offline handwritten signatures of different sizes"
    - "offline handwritten signature verification literature review"
    - "learning features for offline handwritten signature verification using deep convolutional neural networks"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-4 (induced by source paper "persian signature verification using fully convolutional networks"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "utsig a persian offline signature dataset"
    - "learning features for offline handwritten signature verification using deep convolutional neural networks"
    - "very deep convolutional neural networks for raw waveforms"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "a comprehensive study of sparse representation techniques for offline signature verification"):
  with members:
    - "fixed sized representation learning from offline handwritten signatures of different sizes"
    - "offline handwritten signature verification literature review"
    - "signet convolutional siamese network for writer independent offline signature verification"
    - "utsig a persian offline signature dataset"
    - "learning features for offline handwritten signature verification using deep convolutional neural networks"
    - "analyzing features learned for offline signature verification using deep cnns"
    - "a survey of sparse representation algorithms and applications"
    - "sparse modeling for image and vision processing"

Connected through hyperedge HE-6 (induced by source paper "fixed sized representation learning from offline handwritten signatures of different sizes"):
  with members:
    - "offline handwritten signature verification literature review"
    - "learning features for offline handwritten signature verification using deep convolutional neural networks"
    - "analyzing features learned for offline signature verification using deep cnns"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "return of the devil in the details delving deep into convolutional nets"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"

Connected through hyperedge HE-7 (induced by source paper "a writer independent approach for offline signature verification using deep convolutional neural networks features"):
  with members:
    - "offline handwritten signature verification literature review"
    - "learning features for offline handwritten signature verification using deep convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "learning features for offline handwritten signature verification using deep convolutional neural networks"):
  with members:
    - "offline handwritten signature verification literature review"
    - "analyzing features learned for offline signature verification using deep cnns"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "deep learning of representations looking forward"

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)