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: "scalenet guiding object proposal generation in supermarkets and beyond"
  Abstract: Motivated by product detection in supermarkets, this paper studies the problem of object proposal generation in supermarket images and other natural images. We argue that estimation of object scales in images is helpful for generating object proposals, especially for supermarket images where object scales are usually within a small range. Therefore, we propose to estimate object scales of images before generating object proposals. The proposed method for predicting object scales is called ScaleNet. To validate the effectiveness of ScaleNet, we build three supermarket datasets, two of which are real-world datasets used for testing and the other one is a synthetic dataset used for training. In short, we extend the previous state-of-the-art object proposal methods by adding a scale prediction phase. The resulted method outperforms the previous state-of-the-art on the supermarket datasets by a large margin. We also show that the approach works for object proposal on other natural images and it outperforms the previous state-of-the-art object proposal methods on the MS COCO dataset. The supermarket datasets, the virtual supermarkets, and the tools for creating more synthetic datasets will be made public.

Connected through hyperedge HE-1 (induced by source paper "msr multi scale shape regression for scene text detection"):
  with members:
    - "rotation sensitive regression for oriented scene text detection"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"
    - "pixellink detecting scene text via instance segmentation"
    - "accurate scene text detection through border semantics awareness and bootstrapping"
    - "textboxes a single shot oriented scene text detector"
    - "mask textspotter an end to end trainable neural network for spotting text with arbitrary shapes"
    - "multi oriented scene text detection via corner localization and region segmentation"
    - "wordsup exploiting word annotations for character based text detection"

Connected through hyperedge HE-2 (induced by source paper "domain invariant hierarchical embedding for grocery products recognition"):
  with members:
    - "deep semantic hashing with generative adversarial networks"
    - "sampling matters in deep embedding learning"
    - "deep learning based large scale visual recommendation and search for e commerce"
    - "product recognition in store shelves as a sub graph isomorphism problem"
    - "prototypical networks for few shot learning"
    - "adversarial discriminative domain adaptation"
    - "learning spread out local feature descriptors"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "neural rejuvenation improving deep network training by enhancing computational resource utilization"):
  with members:
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "deep clustering for unsupervised learning of visual features"
    - "stacked cross attention for image text matching"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "multi scale spatially asymmetric recalibration for image classification"
    - "deep co training for semi supervised image recognition"
    - "knowledge distillation in generations more tolerant teachers educate better students"
    - "efficient neural architecture search via parameter sharing"

Connected through hyperedge HE-4 (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"
    - "instance sensitive fully convolutional networks"
    - "image captioning with semantic attention"

Connected through hyperedge HE-5 (induced by source paper "deep co training for semi supervised image recognition"):
  with members:
    - "multi scale spatially asymmetric recalibration for image classification"
    - "good semi supervised learning that requires a bad gan"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "few shot image recognition by predicting parameters from activations"
    - "sort second order response transform for visual recognition"
    - "gradually updated neural networks for large scale image recognition"
    - "introspective classification with convolutional nets"
    - "single shot object detection with enriched semantics"

Connected through hyperedge HE-6 (induced by source paper "progressive recurrent learning for visual recognition"):
  with members:
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "attention guided curriculum learning for weakly supervised classification and localization of thoracic diseases on chest radiographs"
    - "improved training with curriculum gans"
    - "low shot learning from imaginary data"
    - "sampleahead online classifier sampler communication for learning from synthesized data"
    - "dynamic few shot visual learning without forgetting"
    - "progressive neural architecture search"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"

Connected through hyperedge HE-7 (induced by source paper "a deep learning pipeline for product recognition on store shelves"):
  with members:
    - "deep learning based large scale visual recommendation and search for e commerce"
    - "product recognition in store shelves as a sub graph isomorphism problem"
    - "deep image retrieval learning global representations for image search"
    - "yolo9000 better faster stronger"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "facenet a unified embedding for face recognition and clustering"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-8 (induced by source paper "gradually updated neural networks for large scale image recognition"):
  with members:
    - "skip connections eliminate singularities"
    - "snapshot ensembles train 1 get m for free"
    - "few shot image recognition by predicting parameters from activations"
    - "mixup beyond empirical risk minimization"
    - "dual path networks"
    - "shake shake regularization"
    - "aggregated residual transformations for deep neural networks"
    - "deep networks with stochastic depth"

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)