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 "ocular recognition databases and competitions a survey"):
  Abstract: The use of the iris and periocular region as biometric traits has been extensively investigated, mainly due to the singularity of the iris features and the use of the periocular region when the image resolution is not sufficient to extract iris information. In addition to providing information about an individual's identity, features extracted from these traits can also be explored to obtain other information such as the individual's gender, the influence of drug use, the use of contact lenses, spoofing, among others. This work presents a survey of the databases created for ocular recognition, detailing their protocols and how their images were acquired. We also describe and discuss the most popular ocular recognition competitions (contests), highlighting the submitted algorithms that achieved the best results using only iris trait and also fusing iris and periocular region information. Finally, we describe some relevant works applying deep learning techniques to ocular recognition and point out new challenges and future directions. Considering that there are a large number of ocular databases, and each one is usually designed for a specific problem, we believe this survey can provide a broad overview of the challenges in ocular biometrics.

Includes members:
  Member "deep representations for cross spectral ocular biometrics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spectrum translation for cross spectral ocular matching")
  Member "simultaneous iris and periocular region detection using coarse annotations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep representations for cross spectral ocular biometrics")
  Member "openeds open eye dataset":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "content consistent generation of realistic eyes with style")
  Member "robust iris segmentation based on fully convolutional networks and generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sip segnet a deep convolutional encoder decoder network for joint semantic segmentation and extraction of sclera iris and pupil based on periocular region suppression")
  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 "fully convolutional networks and generative adversarial networks applied to sclera segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sip segnet a deep convolutional encoder decoder network for joint semantic segmentation and extraction of sclera iris and pupil based on periocular region suppression")
  Member "a survey on periocular biometrics research":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sip segnet a deep convolutional encoder decoder network for joint semantic segmentation and extraction of sclera iris and pupil based on periocular region suppression")
  Member "the impact of preprocessing on deep representations for iris recognition on unconstrained environments":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "spectrum translation for cross spectral ocular matching")

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)