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: "deep unsupervised learning through spatial contrasting"
  Abstract: Convolutional networks have marked their place over the last few years as the#N#best performing model for various visual tasks. They are, however, most suited#N#for supervised learning from large amounts of labeled data. Previous attempts#N#have been made to use unlabeled data to improve model performance by applying#N#unsupervised techniques. These attempts require different architectures and training methods.#N#In this work we present a novel approach for unsupervised training#N#of Convolutional networks that is based on contrasting between spatial regions#N#within images.  This criterion can be employed within conventional neural net-#N#works and trained using standard techniques such as SGD and back-propagation,#N#thus complementing supervised methods.

Connected through hyperedge HE-1 (induced by source paper "scattering networks for hybrid representation learning"):
  with members:
    - "generative networks as inverse problems with scattering transforms"
    - "building a regular decision boundary with deep networks"
    - "scaling the scattering transform deep hybrid networks"
    - "look listen and learn"
    - "improved training of wasserstein gans"
    - "inverse problems with invariant multiscale statistics"
    - "what makes imagenet good for transfer learning"
    - "adversarial feature learning"

Connected through hyperedge HE-2 (induced by source paper "self supervised spatiotemporal feature learning via video rotation prediction"):
  with members:
    - "self supervised visual feature learning with deep neural networks a survey"
    - "real world anomaly detection in surveillance videos"
    - "moments in time dataset one million videos for event understanding"
    - "videocapsulenet a simplified network for action detection"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "cross and learn cross modal self supervision"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-3 (induced by source paper "regularization for deep learning a taxonomy"):
  with members:
    - "proximal backpropagation"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "adjusting for dropout variance in batch normalization and weight initialization"
    - "synthesizing robust adversarial examples"
    - "an overview of multi task learning in deep neural networks"
    - "densely connected convolutional networks"
    - "trivializing the energy landscape of deep networks"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-4 (induced by source paper "generative semantic manipulation with contrasting gan"):
  with members:
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "unsupervised image to image translation networks"
    - "perceptual generative adversarial networks for small object detection"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "zm net real time zero shot image manipulation network"
    - "recurrent topic transition gan for visual paragraph generation"
    - "dualgan unsupervised dual learning for image to image translation"
    - "scan structure correcting adversarial network for organ segmentation in chest x rays"

Connected through hyperedge HE-5 (induced by source paper "scaling the scattering transform deep hybrid networks"):
  with members:
    - "building a regular decision boundary with deep networks"
    - "multiscale hierarchical convolutional networks"
    - "inverse problems with invariant multiscale statistics"
    - "what makes imagenet good for transfer learning"
    - "wide residual networks"
    - "learning both weights and connections for efficient neural networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "matterport3d learning from rgb d data in indoor environments"):
  with members:
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "semantic scene completion from a single depth image"
    - "lift learned invariant feature transform"
    - "rgbd datasets past present and future"
    - "structured global registration of rgb d scans in indoor environments"
    - "learning to navigate the energy landscape"
    - "a large dataset of object scans"
    - "physically based rendering for indoor scene understanding using convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "robotic pick and place of novel objects in clutter with multi affordance grasping and cross domain image matching"):
  with members:
    - "segicp integrated deep semantic segmentation and pose estimation"
    - "look ahead before you leap end to end active recognition by forecasting the effect of motion"
    - "supervision via competition robot adversaries for learning tasks"
    - "what makes imagenet good for transfer learning"
    - "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"
    - "analysis and observations from the first amazon picking challenge"
    - "deep residual learning for image recognition"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"

Connected through hyperedge HE-8 (induced by source paper "multimodal classification for analysing social media"):
  with members:
    - "building a large scale dataset for image emotion recognition the fine print and the benchmark"
    - "bag of tricks for efficient text classification"
    - "going deeper with convolutions"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "efficient estimation of word representations in vector space"

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)