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 "feature agglomeration networks for single stage face detection"):
  Abstract: Recent years have witnessed promising results of face detection using deep learning, especially for the family of region-based convolutional neural networks (R-CNN) methods and their variants. Despite making remarkable progresses, face detection in the wild remains an open research challenge especially when detecting faces at vastly different scales and characteristics. In this paper, we propose a novel framework of "Feature Agglomeration Networks" (FAN) to build a new single stage face detector, which not only achieves state-of-the-art performance but also runs efficiently. As inspired by the recent success of Feature Pyramid Networks (FPN) \cite{lin2016fpn} for generic object detection, the core idea of our framework is to exploit inherent multi-scale features of a single convolutional neural network to detect faces of varied scales and characteristics by aggregating higher-level semantic feature maps of different scales as contextual cues to augment lower-level feature maps via a hierarchical agglomeration manner at marginal extra computation cost. Unlike the existing FPN approach, we construct our FAN architecture using a new {\it Agglomerative Connection} module and further propose a {\it Hierarchical Loss} to effectively train the FAN model. We evaluate the proposed FAN detector on several public face detection benchmarks and achieved new state-of-the-art results with real-time detection speed on GPU.

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 "dssd deconvolutional single shot detector":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "detecting faces using region based fully convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "kpnet towards minimal face detector")
  Member "face detection through scale friendly deep convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "kpnet towards minimal face detector")
  Member "face detection using deep learning an improved faster rcnn approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "finding interesting frames in deep video analytics a top k approach")
  Member "ssh single stage headless face detector":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "kpnet towards minimal face detector")
  Member "ron reverse connection with objectness prior networks for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pixel semantic revise of position learning a one stage object detector with a shared encoder decoder")
  Member "face r cnn":
    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)