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: "improving object detection with deep convolutional networks via bayesian optimization and structured prediction"
  Abstract: Object detection systems based on the deep convolutional neural network (CNN) have recently made ground-breaking advances on several object detection benchmarks. While the features learned by these high-capacity neural networks are discriminative for categorization, inaccurate localization is still a major source of error for detection. Building upon high-capacity CNN architectures, we address the localization problem by 1) using a search algorithm based on Bayesian optimization that sequentially proposes candidate regions for an object bounding box, and 2) training the CNN with a structured loss that explicitly penalizes the localization inaccuracy. In experiments, we demonstrate that each of the proposed methods improves the detection performance over the baseline method on PASCAL VOC 2007 and 2012 datasets. Furthermore, two methods are complementary and significantly outperform the previous state-of-the-art when combined.

Connected through hyperedge HE-1 (induced by source paper "adaptively denoising proposal collection for weakly supervised object localization"):
  with members:
    - "stacked wasserstein autoencoder"
    - "direct visual inertial odometry with semi dense mapping"
    - "unsupervised deep feature transfer for low resolution image classification"
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "towards learning affine invariant representations via data efficient cnns"
    - "mdcn multi scale deep inception convolutional neural networks for efficient object detection"
    - "learning depth from single images with deep neural network embedding focal length"
    - "soft nms improving object detection with one line of code"

Connected through hyperedge HE-2 (induced by source paper "road crack detection using deep convolutional neural network and adaptive thresholding"):
  with members:
    - "using dp towards a shortest path problem related application"
    - "real time dense stereo embedded in a uav for road inspection"
    - "real time subpixel fast bilateral stereo"
    - "road surface 3d reconstruction based on dense subpixel disparity map estimation"
    - "real time stereo vision based lane detection system"
    - "real time stereo vision for road surface 3 d reconstruction"
    - "a novel disparity transformation algorithm for road segmentation"
    - "improving computer aided detection using convolutional neural networks and random view aggregation"

Connected through hyperedge HE-3 (induced by source paper "a survey of deep learning techniques for mobile robot applications"):
  with members:
    - "sparse then dense alignment based 3d map reconstruction method for endoscopic capsule robots"
    - "unsupervised odometry and depth learning for endoscopic capsule robots"
    - "deep endovo a recurrent convolutional neural network rcnn based visual odometry approach for endoscopic capsule robots"
    - "magnetic visual sensor fusion based dense 3d reconstruction and localization for endoscopic capsule robots"
    - "endo vmfusenet deep visual magnetic sensor fusion approach for uncalibrated unsynchronized and asymmetric endoscopic capsule robot localization data"
    - "endosensorfusion particle filtering based multi sensory data fusion with switching state space model for endoscopic capsule robots"
    - "a fully dense and globally consistent 3d map reconstruction approach for gi tract to enhance therapeutic relevance of the endoscopic capsule robot"
    - "a non rigid map fusion based direct slam method for endoscopic capsule robots"

Connected through hyperedge HE-4 (induced by source paper "unsupervised discovery of object landmarks as structural representations"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "learning to generate long term future via hierarchical prediction"
    - "convolutional neural network architecture for geometric matching"
    - "anchornet a weakly supervised network to learn geometry sensitive features for semantic matching"
    - "simultaneous facial landmark detection pose and deformation estimation under facial occlusion"
    - "unsupervised part learning for visual recognition"
    - "mask r cnn"

Connected through hyperedge HE-5 (induced by source paper "visual object categorization based on hierarchical shape motifs learned from noisy point cloud decompositions"):
  with members:
    - "geometric deep learning going beyond euclidean data"
    - "deepskeleton learning multi task scale associated deep side outputs for object skeleton extraction in natural images"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-6 (induced by source paper "discriminative bimodal networks for visual localization and detection with natural language queries"):
  with members:
    - "segmentation from natural language expressions"
    - "generative adversarial text to image synthesis"
    - "grounding of textual phrases in images by reconstruction"
    - "modeling context between objects for referring expression understanding"
    - "modeling context in referring expressions"
    - "r fcn object detection via region based fully convolutional networks"
    - "generation and comprehension of unambiguous object descriptions"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-7 (induced by source paper "dynamic zoom in network for fast object detection in large images"):
  with members:
    - "remotenet efficient relevant motion event detection for large scale home surveillance videos"
    - "nisp pruning networks using neuron importance score propagation"
    - "c wsl count guided weakly supervised localization"
    - "visual relationship detection with internal and external linguistic knowledge distillation"
    - "mask r cnn"
    - "generating holistic 3d scene abstractions for text based image retrieval"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-8 (induced by source paper "co localization with category consistent features and geodesic distance propagation"):
  with members:
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "bayesian joint topic modelling for weakly supervised object localisation"
    - "image co localization by mimicking a good detector s confidence score distribution"
    - "ssd single shot multibox detector"
    - "weakly supervised localization using deep feature maps"
    - "geodesic distance histogram feature for video segmentation"
    - "you only look once unified real time object detection"
    - "relaxed multiple instance svm with application to object discovery"

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)