human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a comprehensive study of sparse representation techniques for offline signature verification"):
  Abstract: In this work, a feature extraction method for offline signature verification is presented that harnesses the power of sparse representation in order to deliver state-of-the-art verification performance in several signature datasets like CEDAR, MCYT-75, GPDS and UTSIG. Beyond the accuracy improvements, several major parameters associated with sparse representation; such as selected configuration, dictionary size, sparsity level and positivity priors are investigated. Besides, it is evinced that 2nd order statistics of the sparse codes is a powerful pooling function for the formation of the global signature descriptor. Also, a thorough evaluation of the effects of preprocessing is introduced by an automated algorithm in order to select the optimum thinning level. Finally, a segmentation strategy which employs a special form of spatial pyramid tailored to the problem of sparse representation is presented along with the enhancing of the produced descriptor on meaningful areas of the signature as emerged from the BRISK key-point detection mechanism. The obtained state-of-the-art results on the most challenging signature datasets provide a strong indication towards the benefits of learned features, even in writer dependent (WD) scenarios with a unique model for each writer and only a few available reference samples of him/her.

Includes members:
  Member "fixed sized representation learning from offline handwritten signatures of different sizes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi representational learning for offline signature verification using multi loss snapshot ensemble of cnns")
  Member "offline handwritten signature verification literature review":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi representational learning for offline signature verification using multi loss snapshot ensemble of cnns")
  Member "signet convolutional siamese network for writer independent offline signature verification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "featurenms non maximum suppression by learning feature embeddings")
  Member "utsig a persian offline signature dataset":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi representational learning for offline signature verification using multi loss snapshot ensemble of cnns")
  Member "learning features for offline handwritten signature verification using deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "character independent font identification")
  Member "writer independent feature learning for offline signature verification using deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "biometric recognition using deep learning a survey")
  Member "analyzing features learned for offline signature verification using deep cnns":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi representational learning for offline signature verification using multi loss snapshot ensemble of cnns")
  Member "a survey of sparse representation algorithms and applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "joint power allocation and load balancing optimization for energy efficient cell free massive mimo networks")

Question: Please tell me which class the source paper 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)