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: "s 3 fd single shot scale invariant face detector"
  Abstract: This paper presents a real-time face detector, named Single Shot Scale-invariant Face Detector (S$^3$FD), which performs superiorly on various scales of faces with a single deep neural network, especially for small faces. Specifically, we try to solve the common problem that anchor-based detectors deteriorate dramatically as the objects become smaller. We make contributions in the following three aspects: 1) proposing a scale-equitable face detection framework to handle different scales of faces well. We tile anchors on a wide range of layers to ensure that all scales of faces have enough features for detection. Besides, we design anchor scales based on the effective receptive field and a proposed equal proportion interval principle; 2) improving the recall rate of small faces by a scale compensation anchor matching strategy; 3) reducing the false positive rate of small faces via a max-out background label. As a consequence, our method achieves state-of-the-art detection performance on all the common face detection benchmarks, including the AFW, PASCAL face, FDDB and WIDER FACE datasets, and can run at 36 FPS on a Nvidia Titan X (Pascal) for VGA-resolution images.

Connected through hyperedge HE-1 (induced by source paper "msfd multi scale receptive field face detector"):
  with members:
    - "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"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-2 (induced by source paper "face recognition in unconstrained conditions a systematic review"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "focal loss for dense object detection"
    - "face attention network an effective face detector for the occluded faces"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "face detection through scale friendly deep convolutional networks"
    - "cross age lfw a database for studying cross age face recognition in unconstrained environments"
    - "ssh single stage headless face detector"

Connected through hyperedge HE-3 (induced by source paper "r 2 cnn fast tiny object detection in large scale remote sensing images"):
  with members:
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "a survey on object detection in optical remote sensing images"
    - "ssd single shot multibox detector"
    - "training region based object detectors with online hard example mining"
    - "feature pyramid networks for object detection"
    - "pyramid scene parsing network"
    - "finding tiny faces"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-4 (induced by source paper "deepprivacy a generative adversarial network for face anonymization"):
  with members:
    - "sc fegan face editing generative adversarial network with user s sketch and color"
    - "dsfd dual shot face detector"
    - "learning to anonymize faces for privacy preserving action detection"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "generative face completion"

Connected through hyperedge HE-5 (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"
    - "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"
    - "synthesizing robust adversarial examples"

Connected through hyperedge HE-6 (induced by source paper "metaanchor learning to detect objects with customized anchors"):
  with members:
    - "detnet a backbone network for object detection"
    - "deep perm set net learn to predict sets with unknown permutation and cardinality using deep neural networks"
    - "sface an efficient network for face detection in large scale variations"
    - "beyond trade off accelerate fcn based face detector with higher accuracy"
    - "fssd feature fusion single shot multibox detector"
    - "focal loss for dense object detection"
    - "what can help pedestrian detection"
    - "dssd deconvolutional single shot detector"

Connected through hyperedge HE-7 (induced by source paper "pyramidbox a context assisted single shot face detector"):
  with members:
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "focal loss for dense object detection"
    - "single shot refinement neural network for object detection"
    - "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"
    - "face detection with end to end integration of a convnet and a 3d model"

Connected through hyperedge HE-8 (induced by source paper "to learn image super resolution use a gan to learn how to do image degradation first"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "attention aware face hallucination via deep reinforcement learning"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "enhanced deep residual networks for single image super resolution"

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)