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 "fingerprint synthesis search with 100 million prints"):
  Abstract: Evaluation of large-scale fingerprint search algorithms has been limited due to lack of publicly available datasets. A solution to this problem is to synthesize a dataset of fingerprints with characteristics similar to those of real fingerprints. We propose a Generative Adversarial Network (GAN) to synthesize a fingerprint dataset consisting of 100 million fingerprint images. In comparison to published methods, our approach incorporates an identity loss which guides the generator to synthesize a diverse set of fingerprints corresponding to more distinct identities. To demonstrate that the characteristics of our synthesized fingerprints are similar to those of real fingerprints, we show that (i) the NFIQ quality value distribution of the synthetic fingerprints follows the corresponding distribution of real fingerprints and (ii) the synthetic fingerprints are more distinct than existing synthetic fingerprints (and more closely align with the distinctiveness of real fingerprints). We use our synthesis algorithm to generate 100 million fingerprint images in 17.5 hours on 100 Tesla K80 GPUs when executed in parallel. Finally, we report for the first time in open literature, search accuracy (DeepPrint rank-1 accuracy of 91.4%) against a gallery of 100 million fingerprint images (using 2,000 NIST SD4 rolled prints as the queries).

Includes members:
  Member "learning a fixed length fingerprint representation":
    also connected through other hyperedges:
      (none)
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "improved training of wasserstein gans":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "deepmasterprints generating masterprints for dictionary attacks via latent variable evolution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "increasing generality in machine learning through procedural content generation")
  Member "unsupervised representation learning with deep convolutional generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "separating the real from the synthetic minutiae histograms as fingerprints of fingerprints":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "perfect fingerprint orientation fields by locally adaptive global models")

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)