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 "end to end latent fingerprint search"):
  Abstract: Latent fingerprints are one of the most important and widely used sources of evidence in law enforcement and forensic agencies. Yet the performance of the state-of-the-art latent recognition systems is far from satisfactory, and they often require manual markups to boost the latent search performance. Further, the COTS systems are proprietary and do not output the true comparison scores between a latent and reference prints to conduct quantitative evidential analysis. We present an end-to-end latent fingerprint search system, including automated region of interest (ROI) cropping, latent image preprocessing, feature extraction, feature comparison , and outputs a candidate list. Two separate minutiae extraction models provide complementary minutiae templates. To compensate for the small number of minutiae in small area and poor quality latents, a virtual minutiae set is generated to construct a texture template. A 96-dimensional descriptor is extracted for each minutia from its neighborhood. For computational efficiency, the descriptor length for virtual minutiae is further reduced to 16 using product quantization. Our end-to-end system is evaluated on three latent databases: NIST SD27 (258 latents); MSP (1,200 latents), WVU (449 latents) and N2N (10,000 latents) against a background set of 100K rolled prints, which includes the true rolled mates of the latents with rank-1 retrieval rates of 65.7%, 69.4%, 65.5%, and 7.6% respectively. A multi-core solution implemented on 24 cores obtains 1ms per latent to rolled comparison.

Includes members:
  Member "automatic latent fingerprint segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning a fixed length fingerprint representation")
  Member "latent fingerprint recognition role of texture template":
    also connected through other hyperedges:
      (none)
  Member "on the intrinsic dimensionality of face representation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "occlusion guided compact template learning for ensemble deep network based pose invariant face recognition")
  Member "automated latent fingerprint recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fingerprints fixed length representation via deep networks and domain knowledge")
  Member "robust minutiae extractor integrating deep networks and fingerprint domain knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "universal material translator towards spoof fingerprint generalization")
  Member "fingernet an unified deep network for fingerprint minutiae extraction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "demographic bias in biometrics a survey on an emerging challenge")
  Member "latent fingerprint minutia extraction using fully convolutional network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "finger gan generating realistic fingerprint images using connectivity imposed gan")
  Member "deepfool a simple and accurate method to fool deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial ranking attack and defense")

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)