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: "simultaneous iris and periocular region detection using coarse annotations"
  Abstract: In this work, we propose to detect the iris and periocular regions simultaneously using coarse annotations and two well-known object detectors: YOLOv2 and Faster R-CNN. We believe coarse annotations can be used in recognition systems based on the iris and periocular regions, given the much smaller engineering effort required to manually annotate the training images. We manually made coarse annotations of the iris and periocular regions (122K images from the visible (VIS) spectrum and 38K images from the near-infrared (NIR) spectrum). The iris annotations in the NIR databases were generated semi-automatically by first applying an iris segmentation CNN and then performing a manual inspection. These annotations were made for 11 well-known public databases (3 NIR and 8 VIS) designed for the iris-based recognition problem and are publicly available to the research community. Experimenting our proposal on these databases, we highlight two results. First, the Faster R-CNN + Feature Pyramid Network (FPN) model reported an Intersection over Union (IoU) higher than YOLOv2 (91.86% vs 85.30%). Second, the detection of the iris and periocular regions being performed simultaneously is as accurate as performed separately, but with a lower computational cost, i.e., two tasks were carried out at the cost of one.

Connected through hyperedge HE-1 (induced by source paper "deep representations for cross spectral ocular biometrics"):
  with members:
    - "an efficient and layout independent automatic license plate recognition system based on the yolo detector"
    - "robust iris segmentation based on fully convolutional networks and generative adversarial networks"
    - "a benchmark for iris location and a deep learning detector evaluation"
    - "fully convolutional networks and generative adversarial networks applied to sclera segmentation"
    - "the impact of preprocessing on deep representations for iris recognition on unconstrained environments"
    - "a robust real time automatic license plate recognition based on the yolo detector"
    - "periocular recognition using cnn features off the shelf"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-2 (induced by source paper "cnn hyperparameter tuning applied to iris liveness detection"):
  with members:
    - "biometric presentation attack detection beyond the visible spectrum"
    - "an efficient and layout independent automatic license plate recognition system based on the yolo detector"
    - "convolutional neural networks for automatic meter reading"
    - "a benchmark for iris location and a deep learning detector evaluation"
    - "a robust real time automatic license plate recognition based on the yolo detector"
    - "presentation attack detection for iris recognition an assessment of the state of the art"
    - "deep representations for iris face and fingerprint spoofing detection"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "ocular recognition databases and competitions a survey"):
  with members:
    - "deep representations for cross spectral ocular biometrics"
    - "openeds open eye dataset"
    - "robust iris segmentation based on fully convolutional networks and generative adversarial networks"
    - "a benchmark for iris location and a deep learning detector evaluation"
    - "fully convolutional networks and generative adversarial networks applied to sclera segmentation"
    - "a survey on periocular biometrics research"
    - "the impact of preprocessing on deep representations for iris recognition on unconstrained environments"
    - "vggface2 a dataset for recognising faces across pose and age"

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)