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: "the beauty of capturing faces rating the quality of digital portraits"
  Abstract: Digital portrait photographs are everywhere, and while the number of face pictures keeps growing, not much work has been done to on automatic portrait beauty assessment. In this paper, we design a specific framework to automatically evaluate the beauty of digital portraits. To this end, we procure a large dataset of face images annotated not only with aesthetic scores but also with information about the traits of the subject portrayed. We design a set of visual features based on portrait photography literature, and extensively analyze their relation with portrait beauty, exposing interesting findings about what makes a portrait beautiful. We find that the beauty of a portrait is linked to its artistic value, and independent from age, race and gender of the subject. We also show that a classifier trained with our features to separate beautiful portraits from non-beautiful portraits outperforms generic aesthetic classifiers.

Connected through hyperedge HE-1 (induced by source paper "aesthetic quality assessment for group photograph"):
  with members:
    - "an universal image attractiveness ranking framework"
    - "nima neural image assessment"
    - "ilgnet inception modules with connected local and global features for efficient image aesthetic quality classification using domain adaptation"
    - "photo aesthetics ranking network with attributes and content adaptation"
    - "will people like your image learning the aesthetic space"

Connected through hyperedge HE-2 (induced by source paper "beholder gan generation and beautification of facial images with conditioning on their beauty level"):
  with members:
    - "ganimation anatomically aware facial animation from a single image"
    - "transferring rich deep features for facial beauty prediction"
    - "scut fbp5500 a diverse benchmark dataset for multi paradigm facial beauty prediction"
    - "progressive growing of gans for improved quality stability and variation"
    - "face aging with conditional generative adversarial networks"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-3 (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"
    - "an image is worth more than a thousand favorites surfacing the hidden beauty of flickr pictures"

Connected through hyperedge HE-4 (induced by source paper "scut fbp5500 a diverse benchmark dataset for multi paradigm facial beauty prediction"):
  with members:
    - "aggregated residual transformations for deep neural networks"
    - "scut fbp a benchmark dataset for facial beauty perception"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (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"
    - "an image is worth more than a thousand favorites surfacing the hidden beauty of flickr pictures"
    - "imagenet large scale visual recognition challenge"
    - "6 seconds of sound and vision creativity in micro videos"

Connected through hyperedge HE-6 (induced by source paper "multilingual visual sentiment concept matching"):
  with members:
    - "a fast and accurate unconstrained face detector"
    - "visual affect around the world a large scale multilingual visual sentiment ontology"
    - "visual word2vec vis w2v learning visually grounded word embeddings using abstract scenes"
    - "combining language and vision with a multimodal skip gram model"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "multilingual models for compositional distributed semantics"
    - "explain images with multimodal recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "to click or not to click automatic selection of beautiful thumbnails from videos"):
  with members:
    - "video2gif automatic generation of animated gifs from video"
    - "imagenet large scale visual recognition challenge"
    - "6 seconds of sound and vision creativity in micro videos"

Connected through hyperedge HE-8 (induced by source paper "like partying your face says it all predicting the ambiance of places with profile pictures"):
  with members:
    - "6 seconds of sound and vision creativity in micro videos"

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)