human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "face magnet magnifying feature maps to detect small faces"):
  Abstract: In this paper, we introduce the Face Magnifier Network (Face-MageNet), a face detector based on the Faster-RCNN framework which enables the flow of discriminative information of small scale faces to the classifier without any skip or residual connections. To achieve this, Face-MagNet deploys a set of ConvTranspose, also known as deconvolution, layers in the Region Proposal Network (RPN) and another set before the Region of Interest (RoI) pooling layer to facilitate detection of finer faces. In addition, we also design, train, and evaluate three other well-tuned architectures that represent the conventional solutions to the scale problem: context pooling, skip connections, and scale partitioning. Each of these three networks achieves comparable results to the state-of-the-art face detectors. With extensive experiments, we show that Face-MagNet based on a VGG16 architecture achieves better results than the recently proposed ResNet101-based HR method on the task of face detection on WIDER dataset and also achieves similar results on the hard set as our other method SSH.

Includes members:
  Member "cms rcnn contextual multi scale region based cnn for unconstrained face detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "retinaface single stage dense face localisation in the wild")
  Member "ssh single stage headless face detector":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "kpnet towards minimal face detector")
  Member "to boost or not to boost on the limits of boosted trees for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "retinaface single stage dense face localisation in the wild")
  Member "face detection with end to end integration of a convnet and a 3d model":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning for face recognition a critical analysis")
  Member "a unified multi scale deep convolutional neural network for fast object detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "face detection with the faster r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep learning for face recognition a critical analysis")
  Member "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "two stream aural visual affect analysis in the wild")
  Member "joint face detection and alignment using multitask cascaded convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")

Question: Please tell me which class the source paper 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)