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: "signet convolutional siamese network for writer independent offline signature verification"
  Abstract: Offline signature verification is one of the most challenging tasks in biometrics and document forensics. Unlike other verification problems, it needs to model minute but critical details between genuine and forged signatures, because a skilled falsification might often resembles the real signature with small deformation. This verification task is even harder in writer independent scenarios which is undeniably fiscal for realistic cases. In this paper, we model an offline writer independent signature verification task with a convolutional Siamese network. Siamese networks are twin networks with shared weights, which can be trained to learn a feature space where similar observations are placed in proximity. This is achieved by exposing the network to a pair of similar and dissimilar observations and minimizing the Euclidean distance between similar pairs while simultaneously maximizing it between dissimilar pairs. Experiments conducted on cross-domain datasets emphasize the capability of our network to model forgery in different languages (scripts) and handwriting styles. Moreover, our designed Siamese network, named SigNet, exceeds the state-of-the-art results on most of the benchmark signature datasets, which paves the way for further research in this direction.

Connected through hyperedge HE-1 (induced by source paper "featurenms non maximum suppression by learning feature embeddings"):
  with members:
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "large batch optimization for deep learning training bert in 76 minutes"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "yolov3 an incremental improvement"
    - "crowdhuman a benchmark for detecting human in a crowd"
    - "sampling matters in deep embedding learning"
    - "deep learning based large scale visual recommendation and search for e commerce"
    - "soft nms improving object detection with one line of code"

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 "whitenet phishing website detection by visual whitelists"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "attributing fake images to gans learning and analyzing gan fingerprints"
    - "detecting homoglyph attacks with a siamese neural network"
    - "phish iris a new approach for vision based brand prediction of phishing web pages via compact visual descriptors"
    - "deltaphish detecting phishing webpages in compromised websites"
    - "adversarial machine learning at scale"
    - "facenet a unified embedding for face recognition and clustering"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-4 (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"
    - "utsig a persian offline signature dataset"
    - "learning features for offline handwritten signature verification using deep convolutional neural networks"
    - "active transfer learning with zero shot priors reusing past datasets for future tasks"
    - "deep residual learning for image recognition"

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"
    - "utsig a persian offline signature dataset"
    - "learning features for offline handwritten signature verification using deep convolutional neural networks"
    - "writer independent feature learning for offline 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 "hybrid feature learning for handwriting verification"):
  with members:
    - "sift meets cnn a decade survey of instance retrieval"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "a practical guide to cnns and fisher vectors for image instance retrieval"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "ordinal regression using noisy pairwise comparisons for body mass index range estimation"):
  with members:
    - "maximum selection and ranking under noisy comparisons"
    - "face to bmi using computer vision to infer body mass index on social media"
    - "rankiqa learning from rankings for no reference image quality assessment"
    - "learning to compare image patches via convolutional neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "on usage of autoencoders and siamese networks for online handwritten signature verification"):
  with members:
    - "offline handwritten signature verification literature review"
    - "draw a recurrent neural network for image generation"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)