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: "bootstrapping face detection with hard negative examples"
  Abstract: Recently significant performance improvement in face detection was made possible by deeply trained convolutional networks. In this report, a novel approach for training state-of-the-art face detector is described. The key is to exploit the idea of hard negative mining and iteratively update the Faster R-CNN based face detector with the hard negatives harvested from a large set of background examples. We demonstrate that our face detector outperforms state-of-the-art detectors on the FDDB dataset, which is the de facto standard for evaluating face detection algorithms.

Connected through hyperedge HE-1 (induced by source paper "refineface refinement neural network for high performance face detection"):
  with members:
    - "robust and high performance face detector"
    - "high level semantic feature detection a new perspective for pedestrian detection"
    - "pyramidbox high performance detector for finding tiny face"
    - "retinaface single stage dense face localisation in the wild"
    - "accurate face detection for high performance"
    - "improved selective refinement network for face detection"
    - "dsfd dual shot face detector"
    - "face detection using improved faster rcnn"

Connected through hyperedge HE-2 (induced by source paper "msfd multi scale receptive field face detector"):
  with members:
    - "s 3 fd single shot scale invariant face detector"
    - "focal loss for dense object detection"
    - "detecting faces using region based fully convolutional networks"
    - "face detection using deep learning an improved faster rcnn approach"
    - "ssh single stage headless face detector"
    - "to boost or not to boost on the limits of boosted trees for object detection"
    - "faceboxes a cpu real time face detector with high accuracy"
    - "face detection with the faster r cnn"

Connected through hyperedge HE-3 (induced by source paper "unsupervised hard example mining from videos for improved object detection"):
  with members:
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "s 3 fd single shot scale invariant face detector"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "focal loss for dense object detection"
    - "illuminating pedestrians via simultaneous detection segmentation"
    - "detecting faces using region based fully convolutional networks"
    - "fine tuning deep cnn models on specific ms coco categories"
    - "face detection using deep learning an improved faster rcnn approach"

Connected through hyperedge HE-4 (induced by source paper "unsupervised video object segmentation with distractor aware online adaptation"):
  with members:
    - "instance embedding transfer to unsupervised video object segmentation"
    - "unsupervised hard example mining from videos for improved object detection"
    - "videomatch matching based video object segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "design pseudo ground truth with motion cue for unsupervised video object segmentation"
    - "pixel level matching for video object segmentation using convolutional neural networks"
    - "video object segmentation without temporal information"
    - "learning video object segmentation with visual memory"

Connected through hyperedge HE-5 (induced by source paper "s 3 fd single shot scale invariant face detector"):
  with members:
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "face detection using deep learning an improved faster rcnn approach"
    - "to boost or not to boost on the limits of boosted trees for object detection"
    - "face detection with end to end integration of a convnet and a 3d model"
    - "face detection with the faster r cnn"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "ssd single shot multibox detector"
    - "a fast and accurate unconstrained face detector"

Connected through hyperedge HE-6 (induced by source paper "joint multi view face alignment in the wild"):
  with members:
    - "a comprehensive performance evaluation of deformable face tracking in the wild"
    - "densereg fully convolutional dense shape regression in the wild"
    - "arcface additive angular margin loss for deep face recognition"
    - "face detection using deep learning an improved faster rcnn approach"
    - "uv gan adversarial facial uv map completion for pose invariant face recognition"
    - "binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources"
    - "robust facial landmark detection under significant head poses and occlusion"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-7 (induced by source paper "lcdet low complexity fully convolutional neural networks for object detection in embedded systems"):
  with members:
    - "face detection using deep learning an improved faster rcnn approach"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "face detection with end to end integration of a convnet and a 3d model"
    - "ristretto hardware oriented approximation of convolutional neural networks"
    - "face detection with the faster r cnn"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "speed accuracy trade offs for modern convolutional object detectors"

Connected through hyperedge HE-8 (induced by source paper "face r cnn"):
  with members:
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "face detection using deep learning an improved faster rcnn approach"
    - "to boost or not to boost on the limits of boosted trees for object detection"
    - "mask r cnn"
    - "face detection with end to end integration of a convnet and a 3d model"
    - "face detection with the faster r cnn"
    - "training region based object detectors with online hard example mining"
    - "feature pyramid networks for 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)