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: "a benchmark for iris location and a deep learning detector evaluation"
  Abstract: The iris is considered as the biometric trait with the highest unique probability. The iris location is an important task for biometrics systems, affecting directly the results obtained in specific applications such as iris recognition, spoofing and contact lenses detection, among others. This work defines the iris location problem as the delimitation of the smallest squared window that encompasses the iris region. In order to build a benchmark for iris location we annotate (iris squared bounding boxes) four databases from different biometric applications and make them publicly available to the community. Besides these 4 annotated databases, we include 2 others from the literature. We perform experiments on these six databases, five obtained with near infra-red sensors and one with visible light sensor. We compare the classical and outstanding Daugman iris location approach with two window based detectors: 1) a sliding window detector based on features from Histogram of Oriented Gradients (HOG) and a linear Support Vector Machines (SVM) classifier; 2) a deep learning based detector fine-tuned from YOLO object detector. Experimental results showed that the deep learning based detector outperforms the other ones in terms of accuracy and runtime (GPUs version) and should be chosen whenever possible.

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"
    - "simultaneous iris and periocular region detection using coarse annotations"
    - "robust iris segmentation based on fully convolutional networks and generative adversarial networks"
    - "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"
    - "simultaneous iris and periocular region detection using coarse annotations"
    - "convolutional neural networks for automatic meter reading"
    - "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 "joint iris segmentation and localization using deep multi task learning framework"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "bam bottleneck attention module"
    - "learning a discriminative feature network for semantic segmentation"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "learning to find eye region landmarks for remote gaze estimation in unconstrained settings"
    - "cbam convolutional block attention module"
    - "focal loss for dense object detection"

Connected through hyperedge HE-4 (induced by source paper "simultaneous iris and periocular region detection using coarse annotations"):
  with members:
    - "joint iris segmentation and localization using deep multi task learning framework"
    - "robust iris segmentation based on fully convolutional networks and generative adversarial networks"
    - "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"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "yolo9000 better faster stronger"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-5 (induced by source paper "ocular recognition databases and competitions a survey"):
  with members:
    - "deep representations for cross spectral ocular biometrics"
    - "simultaneous iris and periocular region detection using coarse annotations"
    - "openeds open eye dataset"
    - "robust iris segmentation based on fully convolutional networks and generative adversarial networks"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "iris recognition with image segmentation employing retrained off the shelf deep neural networks"):
  with members:
    - "unconstrained iris segmentation using convolutional neural networks"
    - "robust iris segmentation based on fully convolutional networks and generative adversarial networks"
    - "dilated residual networks"
    - "yolo9000 better faster stronger"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-7 (induced by source paper "fully convolutional networks and generative adversarial networks applied to sclera segmentation"):
  with members:
    - "segdensenet iris segmentation for pre and post cataract surgery"
    - "an application of cascaded 3d fully convolutional networks for medical image segmentation"
    - "a robust real time automatic license plate recognition based on the yolo detector"
    - "road segmentation in sar satellite images with deep fully convolutional neural networks"
    - "semantic segmentation using adversarial networks"
    - "spatially supervised recurrent convolutional neural networks for visual object tracking"
    - "image to image translation with conditional adversarial networks"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-8 (induced by source paper "robust iris segmentation based on fully convolutional networks and generative adversarial networks"):
  with members:
    - "fully convolutional networks and generative adversarial networks applied to sclera segmentation"
    - "an application of cascaded 3d fully convolutional networks for medical image segmentation"
    - "a robust real time automatic license plate recognition based on the yolo detector"
    - "road segmentation in sar satellite images with deep fully convolutional neural networks"
    - "multinet real time joint semantic reasoning for autonomous driving"
    - "semantic segmentation using adversarial networks"
    - "image to image translation with conditional adversarial networks"
    - "you only look once unified real time object detection"

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)