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: "pvanet lightweight deep neural networks for real time object detection"
  Abstract: In object detection, reducing computational cost is as important as improving accuracy for most practical usages. This paper proposes a novel network structure, which is an order of magnitude lighter than other state-of-the-art networks while maintaining the accuracy. Based on the basic principle of more layers with less channels, this new deep neural network minimizes its redundancy by adopting recent innovations including C.ReLU and Inception structure. We also show that this network can be trained efficiently to achieve solid results on well-known object detection benchmarks: 84.9% and 84.2% mAP on VOC2007 and VOC2012 while the required compute is less than 10% of the recent ResNet-101.

Connected through hyperedge HE-1 (induced by source paper "accelerating object detection by erasing background activations"):
  with members:
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "fast scnn fast semantic segmentation network"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "rethinking the value of network pruning"
    - "focal loss for dense object detection"
    - "3d semantic segmentation with submanifold sparse convolutional networks"
    - "branchynet fast inference via early exiting from deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "convolutional character networks"):
  with members:
    - "textfield learning a deep direction field for irregular scene text detection"
    - "cornernet detecting objects as paired keypoints"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "fots fast oriented text spotting with a unified network"
    - "an end to end textspotter with explicit alignment and attention"
    - "edit probability for scene text recognition"
    - "textnet irregular text reading from images with an end to end trainable network"
    - "mask textspotter an end to end trainable neural network for spotting text with arbitrary shapes"

Connected through hyperedge HE-3 (induced by source paper "pixor real time 3d object detection from point clouds"):
  with members:
    - "focal loss for dense object detection"
    - "east an efficient and accurate scene text detector"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "3d object proposals using stereo imagery for accurate object class detection"
    - "identity mappings in deep residual networks"
    - "vehicle detection from 3d lidar using fully convolutional network"
    - "ssd single shot multibox detector"
    - "vote3deep fast object detection in 3d point clouds using efficient convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "an end to end textspotter with explicit alignment and attention"):
  with members:
    - "fots fast oriented text spotting with a unified network"
    - "towards end to end text spotting with convolutional recurrent neural networks"
    - "east an efficient and accurate scene text detector"
    - "single shot text detector with regional attention"
    - "wetext scene text detection under weak supervision"
    - "deep direct regression for multi oriented scene text detection"
    - "text flow a unified text detection system in natural scene images"
    - "text attentional convolutional neural network for scene text detection"

Connected through hyperedge HE-5 (induced by source paper "attentionmask attentive efficient object proposal generation focusing on small objects"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "zoom out and in network with recursive training for object proposal"
    - "masklab instance segmentation by refining object detection with semantic and direction features"
    - "ron reverse connection with objectness prior networks for object detection"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "mask r cnn"
    - "scalenet guiding object proposal generation in supermarkets and beyond"
    - "instance sensitive fully convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "single shot textspotter with explicit alignment and attention"):
  with members:
    - "fots fast oriented text spotting with a unified network"
    - "towards end to end text spotting with convolutional recurrent neural networks"
    - "single shot text detector with regional attention"
    - "attention based extraction of structured information from street view imagery"
    - "deep direct regression for multi oriented scene text detection"
    - "multi oriented text detection with fully convolutional networks"
    - "accurate text localization in natural image with cascaded convolutional text network"
    - "scene text detection via holistic multi channel prediction"

Connected through hyperedge HE-7 (induced by source paper "real time 2d multi person pose estimation on cpu lightweight openpose"):
  with members:
    - "multiposenet fast multi person pose estimation using pose residual network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dilated residual networks"
    - "convolutional pose machines"
    - "rmpe regional multi person pose estimation"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "microsoft coco common objects in context"

Connected through hyperedge HE-8 (induced by source paper "vision based semantic mapping and localization for autonomous indoor parking"):
  with members:
    - "direct sparse odometry"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "semi dense 3d semantic mapping from monocular slam"
    - "orb slam a versatile and accurate monocular slam system"

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)