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: "large scale deep learning on the yfcc100m dataset"
  Abstract: We present a work-in-progress snapshot of learning with a 15 billion parameter deep learning network on HPC architectures applied to the largest publicly available natural image and video dataset released to-date. Recent advancements in unsupervised deep neural networks suggest that scaling up such networks in both model and training dataset size can yield significant improvements in the learning of concepts at the highest layers. We train our three-layer deep neural network on the Yahoo! Flickr Creative Commons 100M dataset. The dataset comprises approximately 99.2 million images and 800,000 user-created videos from Yahoo's Flickr image and video sharing platform. Training of our network takes eight days on 98 GPU nodes at the High Performance Computing Center at Lawrence Livermore National Laboratory. Encouraging preliminary results and future research directions are presented and discussed.

Connected through hyperedge HE-1 (induced by source paper "ganhopper multi hop gan for unsupervised image to image translation"):
  with members:
    - "transgaga geometry aware unsupervised image to image translation"
    - "carigans unpaired photo to caricature translation"
    - "logan unpaired shape transform in latent overcomplete space"
    - "multimodal unsupervised image to image translation"
    - "improving shape deformation in unsupervised image to image translation"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unsupervised image to image translation networks"

Connected through hyperedge HE-2 (induced by source paper "unsupervised pre training of image features on non curated data"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "revisiting self supervised visual representation learning"
    - "rethinking imagenet pre training"
    - "learning image representations by completing damaged jigsaw puzzles"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "cross pixel optical flow similarity for self supervised learning"

Connected through hyperedge HE-3 (induced by source paper "improving shape deformation in unsupervised image to image translation"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "neural color transfer between images"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "towards the automatic anime characters creation with generative adversarial networks"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "unsupervised creation of parameterized avatars"

Connected through hyperedge HE-4 (induced by source paper "the helmholtz method using perceptual compression to reduce machine learning complexity"):
  with members:
    - "robust physical world attacks on deep learning models"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "opening the black box of deep neural networks via information"
    - "very deep convolutional networks for large scale image recognition"
    - "striving for simplicity the all convolutional net"

Connected through hyperedge HE-5 (induced by source paper "revisiting unreasonable effectiveness of data in deep learning era"):
  with members:
    - "towards accurate multi person pose estimation in the wild"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "what makes imagenet good for transfer learning"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "planet photo geolocation with convolutional neural networks"
    - "adversarial feature learning"
    - "the curious robot learning visual representations via physical interactions"

Connected through hyperedge HE-6 (induced by source paper "eurosat a novel dataset and deep learning benchmark for land use and land cover classification"):
  with members:
    - "patternnet a benchmark dataset for performance evaluation of remote sensing image retrieval"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "multi task learning for segmentation of building footprints with deep neural networks"
    - "remote sensing image scene classification benchmark and state of the art"
    - "towards better exploiting convolutional neural networks for remote sensing scene classification"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "quantifying facial age by posterior of age comparisons"):
  with members:
    - "deep label distribution learning with label ambiguity"
    - "the megaface benchmark 1 million faces for recognition at scale"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "bag of tricks for efficient text classification"):
  with members:
    - "very deep convolutional networks for text classification"
    - "yfcc100m the new data in multimedia research"
    - "efficient character level document classification by combining convolution and recurrent layers"
    - "text understanding from scratch"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"
    - "a reliable effective terascale linear learning system"
    - "feature hashing for large scale multitask learning"

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.LG(Machine Learning)