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 multi scale cascade fully convolutional network face detector"
  Abstract: Face detection is challenging as faces in images could be present at arbitrary locations and in different scales. We propose a three-stage cascade structure based on fully convolutional neural networks (FCNs). It first proposes the approximate locations where the faces may be, then aims to find the accurate location by zooming on to the faces. Each level of the FCN cascade is a multi-scale fully-convolutional network, which generates scores at different locations and in different scales. A score map is generated after each FCN stage. Probable regions of face are selected and fed to the next stage. The number of proposals is decreased after each level, and the areas of regions are decreased to more precisely fit the face. Compared to passing proposals directly between stages, passing probable regions can decrease the number of proposals and reduce the cases where first stage doesn't propose good bounding boxes. We show that by using FCN and score map, the FCN cascade face detector can achieve strong performance on public datasets.

Connected through hyperedge HE-1 (induced by source paper "learning structure via consensus for face segmentation and parsing"):
  with members:
    - "fsgan subject agnostic face swapping and reenactment"
    - "does generative face completion help face recognition"
    - "face mask extraction in video sequence"
    - "learning discriminators as energy networks in adversarial learning"
    - "adversarial structure matching loss for image segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "face completion with semantic knowledge and collaborative adversarial learning"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-2 (induced by source paper "does generative face completion help face recognition"):
  with members:
    - "geometry aware face completion and editing"
    - "identity preserving face completion for large ocular region occlusion"
    - "high resolution face completion with multiple controllable attributes via fully end to end progressive generative adversarial networks"
    - "free form image inpainting with gated convolution"
    - "additive margin softmax for face verification"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "segmentation aware convolutional networks using local attention masks"

Connected through hyperedge HE-3 (induced by source paper "3d objectness estimation via bottom up regret grouping"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "object detection with deep learning a review"
    - "orthographic feature transform for monocular 3d object detection"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "squeezeseg convolutional neural nets with recurrent crf for real time road object segmentation from 3d lidar point cloud"

Connected through hyperedge HE-4 (induced by source paper "detecting lesion bounding ellipses with gaussian proposal networks"):
  with members:
    - "bounding box regression with uncertainty for accurate object detection"
    - "real time rotation invariant face detection with progressive calibration networks"
    - "3d context enhanced region based convolutional neural network for end to end lesion detection"
    - "towards the success rate of one real time unconstrained salient object detection"
    - "focal loss for dense object detection"
    - "dssd deconvolutional single shot detector"
    - "mask r cnn"
    - "learning a rotation invariant detector with rotatable bounding box"

Connected through hyperedge HE-5 (induced by source paper "improving face detection performance with 3d rendered synthetic data"):
  with members:
    - "survey of face detection on low quality images"
    - "training deep face recognition systems with synthetic data"
    - "seeing small faces from robust anchor s perspective"
    - "pyramidbox a context assisted single shot face detector"
    - "ganimation anatomically aware facial animation from a single image"
    - "sface an efficient network for face detection in large scale variations"
    - "pushing the limits of unconstrained face detection a challenge dataset and baseline results"
    - "recent progress of face image synthesis"

Connected through hyperedge HE-6 (induced by source paper "expnet landmark free deep 3d facial expressions"):
  with members:
    - "extreme 3d face reconstruction looking past occlusions"
    - "faceposenet making a case for landmark free face alignment"
    - "do we really need to collect millions of faces for effective face recognition"
    - "pooling faces template based face recognition with pooled face images"
    - "fitting a 3d morphable model to edges a comparison between hard and soft correspondences"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"
    - "learning detailed face reconstruction from a single image"
    - "convolutional experts constrained local model for facial landmark detection"

Connected through hyperedge HE-7 (induced by source paper "faceposenet making a case for landmark free face alignment"):
  with members:
    - "l2 constrained softmax loss for discriminative face verification"
    - "kepler keypoint and pose estimation of unconstrained faces by learning efficient h cnn regressors"
    - "do we really need to collect millions of faces for effective face recognition"
    - "marr revisited 2d 3d alignment via surface normal prediction"
    - "multi directional multi level dual cross patterns for robust face recognition"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"
    - "learning detailed face reconstruction from a single image"
    - "convolutional experts constrained local model for facial landmark 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)