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: "webvision challenge visual learning and understanding with web data"
  Abstract: We present the 2017 WebVision Challenge, a public image recognition challenge designed for deep learning based on web images without instance-level human annotation. Following the spirit of previous vision challenges, such as ILSVRC, Places2 and PASCAL VOC, which have played critical roles in the development of computer vision by contributing to the community with large scale annotated data for model designing and standardized benchmarking, we contribute with this challenge a large scale web images dataset, and a public competition with a workshop co-located with CVPR 2017. The WebVision dataset contains more than $2.4$ million web images crawled from the Internet by using queries generated from the $1,000$ semantic concepts of the benchmark ILSVRC 2012 dataset. Meta information is also included. A validation set and test set containing human annotated images are also provided to facilitate algorithmic development. The 2017 WebVision challenge consists of two tracks, the image classification task on WebVision test set, and the transfer learning task on PASCAL VOC 2012 dataset. In this paper, we describe the details of data collection and annotation, highlight the characteristics of the dataset, and introduce the evaluation metrics.

Connected through hyperedge HE-1 (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"
    - "what uncertainties do we need in bayesian deep learning for computer vision"

Connected through hyperedge HE-2 (induced by source paper "self supervised learning from web data for multimodal retrieval"):
  with members:
    - "learning from barcelona instagram data what locals and tourists post about its neighbourhoods"
    - "exploring the limits of weakly supervised pretraining"
    - "texttopicnet self supervised learning of visual features through embedding images on semantic text spaces"
    - "self supervised learning of visual features through embedding images into text topic spaces"
    - "webvision database visual learning and understanding from web data"
    - "enriching word vectors with subword information"
    - "synthetic data for text localisation in natural images"
    - "understanding neural networks through deep visualization"

Connected through hyperedge HE-3 (induced by source paper "fine grained land use classification at the city scale using ground level images"):
  with members:
    - "building instance classification using street view images"
    - "large scale mapping of human activity using geo tagged videos"
    - "hidden two stream convolutional networks for action recognition"
    - "quantitative comparison of open source data for fine grain mapping of land use"
    - "eurosat a novel dataset and deep learning benchmark for land use and land cover classification"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "adaptive deep pyramid matching for remote sensing scene classification"
    - "disturblabel regularizing cnn on the loss layer"

Connected through hyperedge HE-4 (induced by source paper "curriculumnet weakly supervised learning from large scale web images"):
  with members:
    - "deep learning is robust to massive label noise"
    - "focal loss for dense object detection"
    - "cleannet transfer learning for scalable image classifier training with label noise"
    - "foodnet recognizing foods using ensemble of deep networks"
    - "knowledge guided disambiguation for large scale scene classification with multi resolution cnns"
    - "squeeze and excitation networks"
    - "webvision database visual learning and understanding from web data"
    - "learning from noisy large scale datasets with minimal supervision"

Connected through hyperedge HE-5 (induced by source paper "one shot segmentation in clutter"):
  with members:
    - "video2shop exactly matching clothes in videos to online shopping images"
    - "unsupervised deep object discovery for instance recognition"
    - "deep extreme cut from extreme points to object segmentation"
    - "prototypical networks for few shot learning"
    - "one shot joint colocalization and cosegmentation"
    - "attentive recurrent comparators"
    - "rethinking atrous convolution for semantic image segmentation"
    - "few shot learning through an information retrieval lens"

Connected through hyperedge HE-6 (induced by source paper "learning to learn from web data through deep semantic embeddings"):
  with members:
    - "self supervised learning of visual features through embedding images into text topic spaces"
    - "webvision database visual learning and understanding from web data"
    - "places an image database for deep scene understanding"
    - "enriching word vectors with subword information"
    - "synthetic data for text localisation in natural images"
    - "learning deep structure preserving image text embeddings"
    - "lewis latent embeddings for word images and their semantics"
    - "how transferable are features in deep neural networks"

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)