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: "face detection using deep learning an improved faster rcnn approach"
  Abstract: In this report, we present a new face detection scheme using deep learning and achieve the state-of-the-art detection performance on the well-known FDDB face detetion benchmark evaluation. In particular, we improve the state-of-the-art faster RCNN framework by combining a number of strategies, including feature concatenation, hard negative mining, multi-scale training, model pretraining, and proper calibration of key parameters. As a consequence, the proposed scheme obtained the state-of-the-art face detection performance, making it the best model in terms of ROC curves among all the published methods on the FDDB benchmark.

Connected through hyperedge HE-1 (induced by source paper "finding interesting frames in deep video analytics a top k approach"):
  with members:
    - "blazeit optimizing declarative aggregation and limit queries for neural network based video analytics"
    - "yolov3 an incremental improvement"
    - "focus querying large video datasets with low latency and low cost"
    - "scanner efficient video analysis at scale"
    - "occlusion aware hand pose estimation using hierarchical mixture density network"
    - "abnormality detection and localization in chest x rays using deep convolutional neural networks"
    - "noscope optimizing neural network queries over video at scale"
    - "mask r cnn"

Connected through hyperedge HE-2 (induced by source paper "deep learning for face recognition a critical analysis"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "face attention network an effective face detector for the occluded faces"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "the do s and don ts for cnn based face verification"

Connected through hyperedge HE-3 (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-4 (induced by source paper "hiding faces in plain sight disrupting ai face synthesis with adversarial perturbations"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "towards imperceptible and robust adversarial example attacks against neural networks"
    - "mesonet a compact facial video forgery detection network"
    - "exposing deep fakes using inconsistent head poses"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "exposing deepfake videos by detecting face warping artifacts"
    - "pyramidbox a context assisted single shot face detector"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"

Connected through hyperedge HE-5 (induced by source paper "shape evasion preventing body shape inference of multi stage approaches"):
  with members:
    - "person recognition in personal photo collections"
    - "one pixel attack for fooling deep neural networks"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "video based reconstruction of 3d people models"
    - "fashion is taking shape understanding clothing preference based on body shape from online sources"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "sequential attacks on agents for long term adversarial goals"

Connected through hyperedge HE-6 (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"
    - "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"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-7 (induced by source paper "sfa small faces attention face detector"):
  with members:
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "pose invariant face alignment with a single cnn"
    - "detecting faces using region based fully convolutional networks"
    - "recurrent scale approximation for object detection in cnn"
    - "ssh single stage headless face detector"
    - "face r cnn"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "revisiting a single stage method for face detection"):
  with members:
    - "seeing small faces from robust anchor s perspective"
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "focal loss for dense object detection"
    - "face attention network an effective face detector for the occluded faces"
    - "face detection through scale friendly deep convolutional networks"
    - "ssh single stage headless face detector"
    - "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"

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)