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: "pronet learning to propose object specific boxes for cascaded neural networks"
  Abstract: This paper aims to classify and locate objects accurately and efficiently, without using bounding box annotations. It is challenging as objects in the wild could appear at arbitrary locations and in different scales. In this paper, we propose a novel classification architecture ProNet based on convolutional neural networks. It uses computationally efficient neural networks to propose image regions that are likely to contain objects, and applies more powerful but slower networks on the proposed regions. The basic building block is a multi-scale fully-convolutional network which assigns object confidence scores to boxes at different locations and scales. We show that such networks can be trained effectively using image-level annotations, and can be connected into cascades or trees for efficient object classification. ProNet outperforms previous state-of-the-art significantly on PASCAL VOC 2012 and MS COCO datasets for object classification and point-based localization.

Connected through hyperedge HE-1 (induced by source paper "few example object detection with model communication"):
  with members:
    - "zero shot detection"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "active self paced learning for cost effective and progressive face identification"
    - "collaborative learning for weakly supervised object detection"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "bridging saliency detection to weakly supervised object detection based on self paced curriculum learning"
    - "learning to segment human by watching youtube"

Connected through hyperedge HE-2 (induced by source paper "min entropy latent model for weakly supervised object detection"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "from bow to cnn two decades of texture representation for texture classification"
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "deep learning for generic object detection a survey"
    - "pcl proposal cluster learning for weakly supervised object detection"
    - "label refinery improving imagenet classification through label progression"
    - "deep self taught learning for weakly supervised object localization"
    - "two phase learning for weakly supervised object localization"

Connected through hyperedge HE-3 (induced by source paper "learning a deep convnet for multi label classification with partial labels"):
  with members:
    - "curriculumnet weakly supervised learning from large scale web images"
    - "exploring the limits of weakly supervised pretraining"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "convnets and imagenet beyond accuracy understanding mistakes and uncovering biases"
    - "webvision challenge visual learning and understanding with web data"

Connected through hyperedge HE-4 (induced by source paper "autoloc weakly supervised temporal action localization"):
  with members:
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"
    - "weakly supervised semantic segmentation using web crawled videos"
    - "predictive corrective networks for action detection"
    - "temporal action detection with structured segment networks"
    - "deep self taught learning for weakly supervised object localization"
    - "two phase learning for weakly supervised object localization"

Connected through hyperedge HE-5 (induced by source paper "weakly supervised instance segmentation using class peak response"):
  with members:
    - "min entropy latent model for weakly supervised object detection"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "mask r cnn"
    - "soft proposal networks for weakly supervised object localization"
    - "pixel level encoding and depth layering for instance level semantic labeling"

Connected through hyperedge HE-6 (induced by source paper "zoom out and in network with map attention decision for region proposal and object detection"):
  with members:
    - "learning deep features via congenerous cosine loss for person recognition"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "dssd deconvolutional single shot detector"
    - "deformable convolutional networks"
    - "recurrent scale approximation for object detection in cnn"
    - "stacked hourglass networks for human pose estimation"
    - "deepproposals hunting objects and actions by cascading deep convolutional layers"
    - "scale aware pixelwise object proposal networks"

Connected through hyperedge HE-7 (induced by source paper "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "learning structured sparsity in deep neural networks"
    - "learning the number of neurons in deep networks"
    - "conditional computation in neural networks for faster models"
    - "learning both weights and connections for efficient neural networks"
    - "dynamic capacity networks"
    - "show attend and tell neural image caption generation with visual attention"
    - "accelerating very deep convolutional networks for classification and detection"

Connected through hyperedge HE-8 (induced by source paper "soft proposal networks for weakly supervised object localization"):
  with members:
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "ssd single shot multibox detector"
    - "weakly supervised localization using deep feature maps"
    - "contextlocnet context aware deep network models for weakly supervised localization"
    - "top down neural attention by excitation backprop"
    - "you only look once unified real time object detection"
    - "weakly supervised deep detection networks"
    - "fast r cnn"

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)