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: "learning features for offline handwritten signature verification using deep convolutional neural networks"
  Abstract: We propose formulations for learning features for Offline Signature Verification.A novel method that uses knowledge of forgeries from a subset of users is proposed.Learned features are used to train classifiers for other users (without forgeries).Experiments on GPDS-960 show a large improvement in state-of-the-art.Results in other 3 datasets show that the features generalize without fine-tuning. Verifying the identity of a person using handwritten signatures is challenging in the presence of skilled forgeries, where a forger has access to a persons signature and deliberately attempt to imitate it. In offline (static) signature verification, the dynamic information of the signature writing process is lost, and it is difficult to design good feature extractors that can distinguish genuine signatures and skilled forgeries. This reflects in a relatively poor performance, with verification errors around 7% in the best systems in the literature. To address both the difficulty of obtaining good features, as well as improve system performance, we propose learning the representations from signature images, in a Writer-Independent format, using Convolutional Neural Networks. In particular, we propose a novel formulation of the problem that includes knowledge of skilled forgeries from a subset of users in the feature learning process, that aims to capture visual cues that distinguish genuine signatures and forgeries regardless of the user. Extensive experiments were conducted on four datasets: GPDS, MCYT, CEDAR and Brazilian PUC-PR datasets. On GPDS-160, we obtained a large improvement in state-of-the-art performance, achieving 1.72% Equal Error Rate, compared to 6.97% in the literature. We also verified that the features generalize beyond the GPDS dataset, surpassing the state-of-the-art performance in the other datasets, without requiring the representation to be fine-tuned to each particular dataset.

Connected through hyperedge HE-1 (induced by source paper "character independent font identification"):
  with members:
    - "automatic generation of typographic font from small font subset"
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "glyphgan style consistent font generation based on generative adversarial networks"
    - "serif or sans visual font analytics on book covers and online advertisements"
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "exploring disentangled feature representation beyond face identification"
    - "font identification in historical documents using active learning"
    - "deepfont identify your font from an image"

Connected through hyperedge HE-2 (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-3 (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-4 (induced by source paper "characterizing and evaluating adversarial examples for offline handwritten signature verification"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "on the robustness of the cvpr 2018 white box adversarial example defenses"
    - "black box adversarial attacks with limited queries and information"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "countering adversarial images using input transformations"
    - "synthesizing robust adversarial examples"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-5 (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"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "writer independent feature learning for offline signature verification using deep convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "active transfer learning for persian offline signature verification"):
  with members:
    - "multi representational learning for offline signature verification using multi loss snapshot ensemble of cnns"
    - "learning representations from persian handwriting for offline signature verification a deep transfer learning approach"
    - "offline handwritten signature verification literature review"
    - "signet convolutional siamese network for writer independent offline signature verification"
    - "utsig a persian offline signature dataset"
    - "active transfer learning with zero shot priors reusing past datasets for future tasks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "offline handwritten mathematical symbol recognition utilising deep learning"):
  with members:
    - "handwritten recognition using svm knn and neural network"
    - "symbol detection in online handwritten graphics using faster r cnn"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "cyclical learning rates for training neural networks"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks at constrained time cost"

Connected through hyperedge HE-8 (induced by source paper "learning representations from persian handwriting for offline signature verification a deep transfer learning approach"):
  with members:
    - "active transfer learning for persian offline signature verification"
    - "multi representational learning for offline signature verification using multi loss snapshot ensemble of cnns"
    - "offline handwritten signature verification literature review"
    - "utsig a persian offline signature dataset"
    - "seven deep semi supervised verification networks"
    - "deep residual learning for image recognition"

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)