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: "faceboxes a cpu real time face detector with high accuracy"
  Abstract: Although tremendous strides have been made in face detection, one of the remaining open challenges is to achieve real-time speed on the CPU as well as maintain high performance, since effective models for face detection tend to be computationally prohibitive. To address this challenge, we propose a novel face detector, named FaceBoxes, with superior performance on both speed and accuracy. Specifically, our method has a lightweight yet powerful network structure that consists of the Rapidly Digested Convolutional Layers (RDCL) and the Multiple Scale Convolutional Layers (MSCL). The RDCL is designed to enable FaceBoxes to achieve real-time speed on the CPU. The MSCL aims at enriching the receptive fields and discretizing anchors over different layers to handle faces of various scales. Besides, we propose a new anchor densification strategy to make different types of anchors have the same density on the image, which significantly improves the recall rate of small faces. As a consequence, the proposed detector runs at 20 FPS on a single CPU core and 125 FPS using a GPU for VGA-resolution images. Moreover, the speed of FaceBoxes is invariant to the number of faces. We comprehensively evaluate this method and present state-of-the-art detection performance on several face detection benchmark datasets, including the AFW, PASCAL face, and FDDB.

Connected through hyperedge HE-1 (induced by source paper "kpnet towards minimal face detector"):
  with members:
    - "bottom up object detection by grouping extreme and center points"
    - "pfld a practical facial landmark detector"
    - "dsfd dual shot face detector"
    - "look at boundary a boundary aware face alignment algorithm"
    - "cornernet detecting objects as paired keypoints"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "gradient harmonized single stage detector"

Connected through hyperedge HE-2 (induced by source paper "rnnpool efficient non linear pooling for ram constrained inference"):
  with members:
    - "visual wake words dataset"
    - "extd extremely tiny face detector via iterative filter reuse"
    - "lffd a light and fast face detector for edge devices"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "the state of sparsity in deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "proxylessnas direct neural architecture search on target task and hardware"

Connected through hyperedge HE-3 (induced by source paper "learning preference based similarities from face images using siamese multi task cnns"):
  with members:
    - "scut fbp5500 a diverse benchmark dataset for multi paradigm facial beauty prediction"
    - "smoothgrad removing noise by adding noise"
    - "an overview of multi task learning in deep neural networks"
    - "afif4 deep gender classification based on adaboost based fusion of isolated facial features and foggy faces"
    - "cross stitch networks for multi task learning"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "densely connected convolutional networks"
    - "some like it hot visual guidance for preference prediction"

Connected through hyperedge HE-4 (induced by source paper "refineface refinement neural network for high performance face detection"):
  with members:
    - "robust and high performance face detector"
    - "high level semantic feature detection a new perspective for pedestrian detection"
    - "pyramidbox high performance detector for finding tiny face"
    - "retinaface single stage dense face localisation in the wild"
    - "accurate face detection for high performance"
    - "improved selective refinement network for face detection"
    - "dsfd dual shot face detector"
    - "face detection using improved faster rcnn"

Connected through hyperedge HE-5 (induced by source paper "improved selective refinement network for face detection"):
  with members:
    - "robust and high performance face detector"
    - "rethinking imagenet pre training"
    - "dsfd dual shot face detector"
    - "face detection using improved faster rcnn"
    - "group normalization"
    - "fa rpn floating region proposals for face detection"
    - "real time rotation invariant face detection with progressive calibration networks"
    - "pyramidbox a context assisted single shot face detector"

Connected through hyperedge HE-6 (induced by source paper "learning modulated loss for rotated object detection"):
  with members:
    - "object detection in optical remote sensing images a survey and a new benchmark"
    - "look more than once an accurate detector for text of arbitrary shapes"
    - "reppoints point set representation for object detection"
    - "omnidirectional scene text detection with sequential free box discretization"
    - "foveabox beyond anchor based object detector"
    - "r3det refined single stage detector with feature refinement for rotating object"
    - "hybrid task cascade for instance segmentation"
    - "fcos fully convolutional one stage object detection"

Connected through hyperedge HE-7 (induced by source paper "high level semantic feature detection a new perspective for pedestrian detection"):
  with members:
    - "dsfd dual shot face detector"
    - "cornernet detecting objects as paired keypoints"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "seeing small faces from robust anchor s perspective"
    - "pyramidbox a context assisted single shot face detector"
    - "deep feature pyramid reconfiguration for object detection"
    - "focal loss for dense object detection"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

Connected through hyperedge HE-8 (induced by source paper "accurate face detection for high performance"):
  with members:
    - "robust and high performance face detector"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "pyramidbox high performance detector for finding tiny face"
    - "improved selective refinement network for face detection"
    - "dsfd dual shot face detector"
    - "face detection using improved faster rcnn"
    - "pyramidbox a context assisted single shot face detector"
    - "selective refinement network for high performance face detection"

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)