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: "utsig a persian offline signature dataset"
  Abstract: The pivotal role of datasets in signature verification systems motivates researchers to collect signature samples. Distinct characteristics of Persian signature demand for richer and culture-dependent offline signature datasets. This study introduces a new and public Persian offline signature dataset, UTSig (University of Tehran Persian Signature), that consists of 8280 images from 115 classes. Each class has 27 genuine signatures, 3 opposite-hand signatures, and 42 skilled forgeries made by 6 forgers. Compared with the other public datasets, UTSig has more samples, more classes, and more forgers. The authors considered various variables including signing period, writing instrument, signature box size, and number of observable samples for forgers in the data collection procedure. By careful examination of main characteristics of offline signature datasets, they observe that Persian signatures have fewer numbers of branch points and end points. They propose and evaluate four different training and test setups for UTSig. Results of the authors’ experiments show that training genuine samples along with opposite-hand samples and random forgeries can improve the performance in terms of equal error rate and minimum cost of log likelihood ratio.

Connected through hyperedge HE-1 (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-2 (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"
    - "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-3 (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"
    - "learning features for offline handwritten signature verification using deep convolutional neural networks"
    - "seven deep semi supervised verification networks"
    - "deep residual learning for image recognition"

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"
    - "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"
    - "very deep convolutional neural networks for raw waveforms"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "graph based offline signature verification"):
  with members:
    - "offline signature verification by combining graph edit distance and triplet networks"
    - "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"

Connected through hyperedge HE-6 (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"
    - "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"

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)