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: "an image is worth more than a thousand favorites surfacing the hidden beauty of flickr pictures"
  Abstract: The dynamics of attention in social media tend to obey power laws. Attention concentrates on a relatively small number of popular items and neglecting the vast majority of content produced by the crowd. Although popularity can be an indication of the perceived value of an item within its community, previous research has hinted to the fact that popularity is distinct from intrinsic quality. As a result, content with low visibility but high quality lurks in the tail of the popularity distribution. This phenomenon can be particularly evident in the case of photo-sharing communities, where valuable photographers who are not highly engaged in online social interactions contribute with high-quality pictures that remain unseen. We propose to use a computer vision method to surface beautiful pictures from the immense pool of near-zero-popularity items, and we test it on a large dataset of creative-commons photos on Flickr. By gathering a large crowdsourced ground truth of aesthetics scores for Flickr images, we show that our method retrieves photos whose median perceived beauty score is equal to the most popular ones, and whose average is lower by only 1.5%.

Connected through hyperedge HE-1 (induced by source paper "and now for something completely different visual novelty in an online network of designers"):
  with members:
    - "bam the behance artistic media dataset for recognition beyond photography"
    - "why do men get more attention exploring factors behind success in an online design community"
    - "quantifying creativity in art networks"
    - "going deeper with convolutions"
    - "6 seconds of sound and vision creativity in micro videos"
    - "an extended cencov campbell characterization of conditional information geometry"

Connected through hyperedge HE-2 (induced by source paper "real time video summarization on commodity hardware"):
  with members:
    - "to click or not to click automatic selection of beautiful thumbnails from videos"
    - "video summarization with long short term memory"
    - "deep residual learning for image recognition"
    - "the beauty of capturing faces rating the quality of digital portraits"

Connected through hyperedge HE-3 (induced by source paper "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity"):
  with members:
    - "time matters multi scale temporalization of social media popularity"
    - "science askscience and badscience on the coexistence of highly related communities"
    - "item popularity prediction in e commerce using image quality feature vectors"
    - "beyond exchangeability the chinese voting process"
    - "learning distributed representations of sentences from unlabelled data"
    - "understanding image virality"
    - "all who wander on the prevalence and characteristics of multi community engagement"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "beautiful and damned combined effect of content quality and social ties on user engagement"):
  with members:
    - "to click or not to click automatic selection of beautiful thumbnails from videos"
    - "online actions with offline impact how online social networks influence online and offline user behavior"
    - "the majority illusion in social networks"
    - "photo aesthetics ranking network with attributes and content adaptation"
    - "yfcc100m the new data in multimedia research"
    - "the beauty of capturing faces rating the quality of digital portraits"
    - "imagenet large scale visual recognition challenge"
    - "6 seconds of sound and vision creativity in micro videos"

Connected through hyperedge HE-5 (induced by source paper "beyond classification latent user interests profiling from visual contents analysis"):
  with members:
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "a picture tells a thousand words about you user interest profiling from user generated visual content"
    - "distributed representations of sentences and documents"
    - "caffe convolutional architecture for fast feature embedding"
    - "gender identity and lexical variation in social media"

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.SI(Social and Information Networks)