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 "cnn hyperparameter tuning applied to iris liveness detection"):
  Abstract: The iris pattern has significantly improved the biometric recognition field due to its high level of stability and uniqueness. Such physical feature has played an important role in security and other related areas. However, presentation attacks, also known as spoofing techniques, can be used to bypass the biometric system with artifacts such as printed images, artificial eyes, and textured contact lenses. To improve the security of these systems, many liveness detection methods have been proposed, and the first Internacional Iris Liveness Detection competition was launched in 2013 to evaluate their effectiveness. In this paper, we propose a hyperparameter tuning of the CASIA algorithm, submitted by the Chinese Academy of Sciences to the third competition of Iris Liveness Detection, in 2017. The modifications proposed promoted an overall improvement, with an 8.48% Attack Presentation Classification Error Rate (APCER) and 0.18% Bonafide Presentation Classification Error Rate (BPCER) for the evaluation of the combined datasets. Other threshold values were evaluated in an attempt to reduce the trade-off between the APCER and the BPCER on the evaluated datasets and worked out successfully.

Includes members:
  Member "biometric presentation attack detection beyond the visible spectrum":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deepsign deep on line signature verification")
  Member "an efficient and layout independent automatic license plate recognition system based on the yolo detector":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "can giraffes become birds an evaluation of image to image translation for data generation")
  Member "simultaneous iris and periocular region detection using coarse annotations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep representations for cross spectral ocular biometrics")
  Member "convolutional neural networks for automatic meter reading":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an efficient and layout independent automatic license plate recognition system based on the yolo detector")
  Member "a benchmark for iris location and a deep learning detector evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep representations for cross spectral ocular biometrics")
  Member "a robust real time automatic license plate recognition based on the yolo detector":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep representations for cross spectral ocular biometrics")
  Member "presentation attack detection for iris recognition an assessment of the state of the art":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "demographic bias in presentation attack detection of iris recognition systems")
  Member "deep representations for iris face and fingerprint spoofing detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "biometric presentation attack detection beyond the visible spectrum")

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)