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: "offline handwritten signature verification literature review"
  Abstract: The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). This has demonstrated to be a challenging task, in particular in the offline (static) scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available. Many advancements have been proposed in the literature in the last 5–10 years, most notably the application of Deep Learning methods to learn feature representations from signature images. In this paper, we present how the problem has been handled in the past few decades, analyze the recent advancements in the field, and the potential directions for future research.

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"
    - "in defense of the triplet loss for person re identification"
    - "signet convolutional siamese network for writer independent offline signature verification"

Connected through hyperedge HE-2 (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"
    - "learning features for offline handwritten signature verification using deep convolutional neural networks"
    - "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-3 (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"
    - "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"
    - "active transfer learning with zero shot priors reusing past datasets for future tasks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (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"
    - "utsig a persian offline signature dataset"
    - "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-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"
    - "utsig a persian offline signature dataset"
    - "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"
    - "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"
    - "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-7 (induced by source paper "fixed sized representation learning from offline handwritten signatures of different sizes"):
  with members:
    - "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"
    - "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-8 (induced by source paper "offline signature verification by combining graph edit distance and triplet networks"):
  with members:
    - "deepdiva a highly functional python framework for reproducible experiments"
    - "learning features for offline handwritten signature verification using deep convolutional neural networks"
    - "deep residual learning for image recognition"
    - "learning to compare image patches via convolutional neural networks"
    - "deep metric learning using triplet network"

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)