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 improved faster rcnn"
  Abstract: Faster RCNN has achieved great success for generic object detection including PASCAL object detection and MS COCO object detection. In this report, we propose a detailed designed Faster RCNN method named FDNet1.0 for face detection. Several techniques were employed including multi-scale training, multi-scale testing, light-designed RCNN, some tricks for inference and a vote-based ensemble method. Our method achieves two 1th places and one 2nd place in three tasks over WIDER FACE validation dataset (easy set, medium set, hard set).

Connected through hyperedge HE-1 (induced by source paper "retinaface single stage dense face localisation in the wild"):
  with members:
    - "robust and high performance face detector"
    - "on learning 3d face morphable model from in the wild images"
    - "wider face and pedestrian challenge 2018 methods and results"
    - "improved selective refinement network for face detection"
    - "dense 3d face decoding over 2500fps joint texture shape convolutional mesh decoders"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "dsfd dual shot face detector"
    - "cosface large margin cosine loss for deep face recognition"

Connected through hyperedge HE-2 (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"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-3 (induced by source paper "extd extremely tiny face detector via iterative filter reuse"):
  with members:
    - "depthwise convolution is all you need for learning multiple visual domains"
    - "thundernet towards real time generic object detection"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "improved selective refinement network for face detection"
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "dsfd dual shot face detector"
    - "tiny dsod lightweight object detection for resource restricted usages"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-4 (induced by source paper "improved selective refinement network for face detection"):
  with members:
    - "robust and high performance face detector"
    - "rethinking imagenet pre training"
    - "dsfd dual shot face detector"
    - "group normalization"
    - "fa rpn floating region proposals for face detection"
    - "real time rotation invariant face detection with progressive calibration networks"
    - "pyramidbox a context assisted single shot face detector"
    - "scratchdet exploring to train single shot object detectors from scratch"

Connected through hyperedge HE-5 (induced by source paper "accurate face detection for high performance"):
  with members:
    - "robust and high performance face detector"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "pyramidbox high performance detector for finding tiny face"
    - "improved selective refinement network for face detection"
    - "dsfd dual shot face detector"
    - "pyramidbox a context assisted single shot face detector"
    - "selective refinement network for high performance face detection"
    - "learning better features for face detection with feature fusion and segmentation supervision"

Connected through hyperedge HE-6 (induced by source paper "centerface joint face detection and alignment using face as point"):
  with members:
    - "feature selective anchor free module for single shot object detection"
    - "consistent optimization for single shot object detection"
    - "retinaface single stage dense face localisation in the wild"
    - "lffd a light and fast face detector for edge devices"
    - "objects as points"
    - "bottom up object detection by grouping extreme and center points"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "cornernet detecting objects as paired keypoints"

Connected through hyperedge HE-7 (induced by source paper "dsfd dual shot face detector"):
  with members:
    - "towards highly accurate and stable face alignment for high resolution videos"
    - "arcface additive angular margin loss for deep face recognition"
    - "pyramidbox a context assisted single shot face detector"
    - "detnet a backbone network for object detection"
    - "selective refinement network for high performance face detection"
    - "focal loss for dense object detection"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "face attention network an effective face detector for the occluded faces"

Connected through hyperedge HE-8 (induced by source paper "robust face detection via learning small faces on hard images"):
  with members:
    - "pyramidbox a context assisted single shot face detector"
    - "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"
    - "detecting faces using region based fully convolutional networks"
    - "face detection through scale friendly deep convolutional networks"
    - "an analysis of scale invariance in object detection snip"
    - "ssh single stage headless face detector"

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)