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 "accelerating proposal generation network for fast face detection on mobile devices"):
  Abstract: Face detection is a widely studied problem over the past few decades. Recently, significant improvements have been achieved via the deep neural network, however, it is still challenging to directly apply these techniques to mobile devices for its limited computational power and memory. In this work, we present a proposal generation acceleration framework for real-time face detection. More specifically, we adopt a popular cascaded convolutional neural network (CNN) as the basis, then apply our acceleration approach on the basic framework to speed up the model inference time. We are motivated by the observation that the computation bottleneck of this framework arises from the proposal generation stage, where each level of the dense image pyramid has to go through the network. In this work, we reduce the number of image pyramid levels by utilizing both global and local facial characteristics (i.e., global face and facial parts). Experimental results on public benchmarks WIDER-face and FDDB demonstrate the satisfactory performance and faster speed compared to the state-of-the-arts. %the comparable accuracy to state-of-the-arts with faster speed.

Includes members:
  Member "scale aware face detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "revisiting the sibling head in object detector")
  Member "age progression regression by conditional adversarial autoencoder":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nestedvae isolating common factors via weak supervision")
  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 "joint face detection and alignment using multitask cascaded convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "finding tiny faces":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "kpnet towards minimal face detector")
  Member "multi view face detection using deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "kpnet towards minimal face detector")
  Member "facenet a unified embedding for face recognition and clustering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "wider face a face detection benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generalizing face representation with unlabeled data")

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)