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: "learning visual n grams from web data"
  Abstract: Real-world image recognition systems need to recognize tens of thousands of classes that constitute a plethora of visual concepts. The traditional approach of annotating thousands of images per class for training is infeasible in such a scenario, prompting the use of webly supervised data. This paper explores the training of image-recognition systems on large numbers of images and associated user comments. In particular, we develop visual n-gram models that can predict arbitrary phrases that are relevant to the content of an image. Our visual n-gram models are feed-forward convolutional networks trained using new loss functions that are inspired by n-gram models commonly used in language modeling. We demonstrate the merits of our models in phrase prediction, phrase-based image retrieval, relating images and captions, and zero-shot transfer.

Connected through hyperedge HE-1 (induced by source paper "clusterfit improving generalization of visual representations"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "billion scale semi supervised learning for image classification"
    - "unsupervised pre training of image features on non curated data"
    - "large scale weakly supervised pre training for video action recognition"
    - "revisiting self supervised visual representation learning"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "large scale distributed neural network training through online distillation"

Connected through hyperedge HE-2 (induced by source paper "large scale learning of general visual representations for transfer"):
  with members:
    - "self supervised learning of video induced visual invariances"
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "billion scale semi supervised learning for image classification"
    - "momentum contrast for unsupervised visual representation learning"
    - "an exponential learning rate schedule for deep learning"
    - "do we train on test data purging cifar of near duplicates"
    - "the visual task adaptation benchmark"
    - "data efficient image recognition with contrastive predictive coding"

Connected through hyperedge HE-3 (induced by source paper "large scale weakly supervised pre training for video action recognition"):
  with members:
    - "scsampler sampling salient clips from video for efficient action recognition"
    - "eco efficient convolutional network for online video understanding"
    - "exploring the limits of weakly supervised pretraining"
    - "videos as space time region graphs"
    - "object level visual reasoning in videos"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"

Connected through hyperedge HE-4 (induced by source paper "exploring the limits of weakly supervised pretraining"):
  with members:
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning transferable architectures for scalable image recognition"
    - "billion scale similarity search with gpus"
    - "squeeze and excitation networks"
    - "convnets and imagenet beyond accuracy understanding mistakes and uncovering biases"
    - "towards accurate multi person pose estimation in the wild"
    - "dual path networks"

Connected through hyperedge HE-5 (induced by source paper "fluid annotation a human machine collaboration interface for full image annotation"):
  with members:
    - "panoptic segmentation"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "exploring the limits of weakly supervised pretraining"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "deep extreme cut from extreme points to object segmentation"
    - "extreme clicking for efficient object annotation"
    - "annotating object instances with a polygon rnn"
    - "masklab instance segmentation by refining object detection with semantic and direction features"

Connected through hyperedge HE-6 (induced by source paper "webly supervised joint embedding for cross modal image text retrieval"):
  with members:
    - "exploring stereotypes and biased data with the crowd"
    - "sampling matters in deep embedding learning"
    - "learning two branch neural networks for image text matching tasks"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "dual path convolutional image text embedding with instance loss"
    - "learning robust visual semantic embeddings"
    - "vse improving visual semantic embeddings with hard negatives"
    - "learning social image embedding with deep multimodal attention networks"

Connected through hyperedge HE-7 (induced by source paper "separating self expression and visual content in hashtag supervision"):
  with members:
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "the devil is in the tails fine grained classification in the wild"
    - "understanding infographics through textual and visual tag prediction"
    - "learning from noisy large scale datasets with minimal supervision"
    - "information theoretical label embeddings for large scale image classification"
    - "yfcc100m the new data in multimedia research"
    - "deep residual learning for image recognition"
    - "learning visual features from large weakly supervised data"

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)