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: "a century of portraits a visual historical record of american high school yearbooks"
  Abstract: Many details about our world are not captured in written records because they are too mundane or too abstract to describe in words. Fortunately, since the invention of the camera, an ever-increasing number of photographs capture much of this otherwise lost information. This plethora of artifacts documenting our "visual culture" is a treasure trove of knowledge as yet untapped by historians. We present a dataset of 37,921 frontal-facing American high school yearbook photos that allow us to use computation to glimpse into the historical visual record too voluminous to be evaluated manually. The collected portraits provide a constant visual frame of reference with varying content. We can therefore use them to consider issues such as a decade's defining style elements, or trends in fashion and social norms over time. We demonstrate that our historical image dataset may be used together with weakly-supervised data-driven techniques to perform scalable historical analysis of large image corpora with minimal human effort, much in the same way that large text corpora together with natural language processing revolutionized historians' workflow. Furthermore, we demonstrate the use of our dataset in dating grayscale portraits using deep learning methods.

Connected through hyperedge HE-1 (induced by source paper "learning to generalize one sample at a time with self supervision"):
  with members:
    - "episodic training for domain generalization"
    - "adagraph unifying predictive and continuous domain adaptation through graphs"
    - "self supervised generalisation with meta auxiliary learning"
    - "domain generalization by solving jigsaw puzzles"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "grasp2vec learning object representations from self supervised grasping"
    - "boosting self supervised learning via knowledge transfer"
    - "open set domain adaptation by backpropagation"

Connected through hyperedge HE-2 (induced by source paper "adagraph unifying predictive and continuous domain adaptation through graphs"):
  with members:
    - "unsupervised domain adaptation using feature whitening and consensus loss"
    - "deep visual domain adaptation a survey"
    - "deep cocktail network multi source unsupervised domain adaptation with category shift"
    - "boosting domain adaptation by discovering latent domains"
    - "adaptive deep learning through visual domain localization"
    - "domain generalization with domain specific aggregation modules"
    - "kitting in the wild through online domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "geostyle discovering fashion trends and events"):
  with members:
    - "tile2vec unsupervised representation learning for spatially distributed data"
    - "exploring the limits of weakly supervised pretraining"
    - "recognition in terra incognita"
    - "streetstyle exploring world wide clothing styles from millions of photos"
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"
    - "fashion forward forecasting visual style in fashion"
    - "mask r cnn"
    - "yfcc100m the new data in multimedia research"

Connected through hyperedge HE-4 (induced by source paper "semantic relationships guided representation learning for facial action unit recognition"):
  with members:
    - "deep structure inference network for facial action unit recognition"
    - "contrast oriented deep neural networks for salient object detection"
    - "instance level salient object segmentation"
    - "eac net a region based deep enhancing and cropping approach for facial action unit detection"
    - "gated graph sequence neural networks"
    - "modeling spatial and temporal cues for multi label facial action unit detection"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "scanner efficient video analysis at scale"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "streetstyle exploring world wide clothing styles from millions of photos"
    - "snorkel rapid training data creation with weak supervision"
    - "end to end learning for self driving cars"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "tensorflow a system for large scale machine learning"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "going deeper with convolutions"

Connected through hyperedge HE-6 (induced by source paper "streetstyle exploring world wide clothing styles from millions of photos"):
  with members:
    - "using deep learning and google street view to estimate the demographic makeup of the us"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "deep learning the city quantifying urban perception at a global scale"
    - "the new data and new challenges in multimedia research"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "clothing co parsing by joint image segmentation and labeling"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-7 (induced by source paper "when follow is just one click away understanding twitter follow behavior in the 2016 u s presidential election"):
  with members:
    - "deciphering the 2016 u s presidential campaign in the twitter sphere a comparison of the trumpists and clintonists"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "multi view face detection using deep convolutional neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "do they all look the same deciphering chinese japanese and koreans by fine grained deep learning"):
  with members:
    - "recurrent highway networks"
    - "deciphering the 2016 u s presidential campaign in the twitter sphere a comparison of the trumpists and clintonists"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "multi view face detection using deep convolutional neural networks"
    - "deep residual learning for image recognition"
    - "fitnets hints for thin deep nets"
    - "deep learning face attributes in the wild"
    - "panda pose aligned networks for deep attribute modeling"

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)