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 detection through scale friendly deep convolutional networks"):
  Abstract: In this paper, we share our experience in designing a convolutional network-based face detector that could handle faces of an extremely wide range of scales. We show that faces with different scales can be modeled through a specialized set of deep convolutional networks with different structures. These detectors can be seamlessly integrated into a single unified network that can be trained end-to-end. In contrast to existing deep models that are designed for wide scale range, our network does not require an image pyramid input and the model is of modest complexity. Our network, dubbed ScaleFace, achieves promising performance on WIDER FACE and FDDB datasets with practical runtime speed. Specifically, our method achieves 76.4 average precision on the challenging WIDER FACE dataset and 96% recall rate on the FDDB dataset with 7 frames per second (fps) for 900 * 1300 input image.

Includes members:
  Member "faceness net face detection through deep facial part responses":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deephmap combined projection grouping and correspondence learning for full dof pose estimation")
  Member "to boost or not to boost on the limits of boosted trees for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "deep learning for face recognition a critical analysis")
  Member "face detection with the faster r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "two stream aural visual affect analysis in the wild")
  Member "ssd single shot multibox detector":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "grid loss detecting occluded faces":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deephmap combined projection grouping and correspondence learning for full dof pose estimation")
  Member "finding tiny faces":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "kpnet towards minimal face detector")

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)