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: "an implementation of faster rcnn with study for region sampling"
  Abstract: We adapted the join-training scheme of Faster RCNN framework from Caffe to TensorFlow as a baseline implementation for object detection. Our code is made publicly available. This report documents the simplifications made to the original pipeline, with justifications from ablation analysis on both PASCAL VOC 2007 and COCO 2014. We further investigated the role of non-maximal suppression (NMS) in selecting regions-of-interest (RoIs) for region classification, and found that a biased sampling toward small regions helps performance and can achieve on-par mAP to NMS-based sampling when converged sufficiently.

Connected through hyperedge HE-1 (induced by source paper "automated building image extraction from 360 panoramas for postdisaster evaluation"):
  with members:
    - "automated detection of pre disaster building images from google street view"
    - "towards automated post earthquake inspections with deep learning based condition aware models"
    - "artistic style transfer for videos and spherical images"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-2 (induced by source paper "objects as points"):
  with members:
    - "feature selective anchor free module for single shot object detection"
    - "scale aware trident networks for object detection"
    - "bottom up object detection by grouping extreme and center points"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "cornernet detecting objects as paired keypoints"
    - "yolov3 an incremental improvement"
    - "simple baselines for human pose estimation and tracking"
    - "acquisition of localization confidence for accurate object detection"

Connected through hyperedge HE-3 (induced by source paper "ok vqa a visual question answering benchmark requiring external knowledge"):
  with members:
    - "bilinear attention networks"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "straight to the facts learning knowledge base retrieval for factual visual question answering"
    - "out of the box reasoning with graph convolution nets for factual visual question answering"
    - "inferring and executing programs for visual reasoning"
    - "mutan multimodal tucker fusion for visual question answering"
    - "incorporating external knowledge to answer open domain visual questions with dynamic memory networks"
    - "learning to reason end to end module networks for visual question answering"

Connected through hyperedge HE-4 (induced by source paper "tracking without bells and whistles"):
  with members:
    - "online multi object tracking with dual matching attention networks"
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "features for multi target multi camera tracking and re identification"
    - "trajectory factory tracklet cleaving and re connection by deep siamese bi gru for multiple object tracking"
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "in defense of the triplet loss for person re identification"
    - "tracking the trackers an analysis of the state of the art in multiple object tracking"

Connected through hyperedge HE-5 (induced by source paper "transferable semi supervised 3d object detection from rgb d data"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "weakly supervised instance segmentation using class peak response"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"
    - "weakly and semi supervised panoptic segmentation"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "learning random walk label propagation for weakly supervised semantic segmentation"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"

Connected through hyperedge HE-6 (induced by source paper "slot based image augmentation system for object detection"):
  with members:
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "augmentation for small object detection"
    - "modeling visual context is key to augmenting object detection datasets"
    - "relation networks for object detection"
    - "an analysis of scale invariance in object detection snip"
    - "deep network flow for multi object tracking"
    - "deep residual learning for image recognition"
    - "deepdriving learning affordance for direct perception in autonomous driving"

Connected through hyperedge HE-7 (induced by source paper "deep mangoes from fruit detection to cultivar identification in colour images of mango trees"):
  with members:
    - "soft nms improving object detection with one line of code"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "mask r cnn"
    - "deep fruit detection in orchards"
    - "you only look once unified real time object detection"
    - "fast r cnn"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-8 (induced by source paper "image captioning with integrated bottom up and multi level residual top down attention for game scene understanding"):
  with members:
    - "bottom up and top down attention for image captioning and visual question answering"
    - "areas of attention for image captioning"
    - "attention correctness in neural image captioning"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "self critical sequence training for image captioning"
    - "aligning where to see and what to tell image caption with region based attention and scene factorization"
    - "densecap fully convolutional localization networks for dense captioning"
    - "deep residual learning for image recognition"

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)