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: "cross sensor pore detection in high resolution fingerprint images using unsupervised domain adaptation"
  Abstract: With the emergence of the high-resolution fingerprint sensors, the research community has focused on level-3 fingerprint features especially, pores for providing the next generation automated fingerprint recognition system (AFRS). Following the recent success of the deep learning approaches in various computer vision tasks, researchers have explored learning-based approaches for pore detection in high-resolution fingerprint images. These learning-based approaches provide better performance than the hand-crafted feature-based approaches. However, domain adaptability of existing learning-based pore detection methods has not been examined in the past. In this paper, we present the first study of domain adaptability of existing learning-based pore detection methods. For this purpose, we have generated an in-house ground truth dataset referred to as IITI-HRF-GT by using 1000 dpi fingerprint sensor and evaluated the performance of the existing learning-based pore detection approaches on it. Further, we have also proposed an approach for detecting pores in a cross sensor scenario referred to as DeepDomainPore using unsupervised domain adaptation technique. Specifically, DeepDomainPore is a combination of a convolutional neural network (CNN) based pore detection approach DeepResPore and an unsupervised domain adaptation approach included during the training process. The domain adaptation in the DeepDomainPore is achieved by embedding a gradient reversal layer between the DeepResPore and a domain classifier network. The results of all the existing and the proposed learning-based pore detection approaches are evaluated on IITI-HRF-GT. The DeepDomainPore provides a true detection rate of 88.09%and an F-score of 83.94% on IITI-HRF-GT. Most importantly, the proposed approach achieves state-of-the-art performance on the cross sensor dataset.

Connected through hyperedge HE-1 (induced by source paper "level three synthetic fingerprint generation"):
  with members:
    - "porenet cnn based pore descriptor for high resolution fingerprint recognition"
    - "finger gan generating realistic fingerprint images using connectivity imposed gan"
    - "multimodal unsupervised image to image translation"
    - "unpaired image to image translation using cycle consistent adversarial networks"

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)