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: "will people like your image learning the aesthetic space"
  Abstract: Rating how aesthetically pleasing an image appears is a highly complex matter and depends on a large number of different visual factors. Previous work has tackled the aesthetic rating problem by ranking on a 1-dimensional rating scale, e.g., incorporating handcrafted attributes. In this paper, we propose a rather general approach to automatically map aesthetic pleasingness with all its complexity into an "aesthetic space" to allow for a highly fine-grained resolution. In detail, making use of deep learning, our method directly learns an encoding of a given image into this high-dimensional feature space resembling visual aesthetics. Additionally to the mentioned visual factors, differences in personal judgments have a large impact on the likeableness of a photograph. Nowadays, online platforms allow users to "like" or favor certain content with a single click. To incorporate a huge diversity of people, we make use of such multi-user agreements and assemble a large data set of 380K images (AROD) with associated meta information and derive a score to rate how visually pleasing a given photo is. We validate our derived model of aesthetics in a user study. Further, without any extra data labeling or handcrafted features, we achieve state-of-the art accuracy on the AVA benchmark data set. Finally, as our approach is able to predict the aesthetic quality of any arbitrary image or video, we demonstrate our results on applications for resorting photo collections, capturing the best shot on mobile devices and aesthetic key-frame extraction from videos.

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"
    - "the beauty of capturing faces rating the quality of digital portraits"

Connected through hyperedge HE-2 (induced by source paper "composition aware image aesthetics assessment"):
  with members:
    - "nima neural image assessment"
    - "videos as space time region graphs"
    - "a deep architecture for unified aesthetic prediction"
    - "non local neural networks"
    - "predicting aesthetic score distribution through cumulative jensen shannon divergence"
    - "attend and interact higher order object interactions for video understanding"
    - "image aesthetic assessment an experimental survey"
    - "feastnet feature steered graph convolutions for 3d shape analysis"

Connected through hyperedge HE-3 (induced by source paper "generating persuasive visual storylines for promotional videos"):
  with members:
    - "learning video story composition via recurrent neural network"
    - "nima neural image assessment"
    - "building ethics into artificial intelligence"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "building emotional machines recognizing image emotions through deep neural networks"
    - "learning visual storylines with skipping recurrent neural networks"
    - "sort story sorting jumbled images and captions into stories"
    - "one weird trick for parallelizing convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "effective aesthetics prediction with multi level spatially pooled features"):
  with members:
    - "nima neural image assessment"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "photo aesthetics ranking network with attributes and content adaptation"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "recognizing image style"

Connected through hyperedge HE-5 (induced by source paper "aesthetic based clothing recommendation"):
  with members:
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "item silk road recommending items from information domains to social users"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "birank towards ranking on bipartite graphs"
    - "a lamp adaptive layout aware multi patch deep convolutional neural network for photo aesthetic assessment"
    - "a visual and textual recurrent neural network for sequential prediction"
    - "brain inspired deep networks for image aesthetics assessment"

Connected through hyperedge HE-6 (induced by source paper "understanding image quality and trust in peer to peer marketplaces"):
  with members:
    - "bam the behance artistic media dataset for recognition beyond photography"
    - "ssd single shot multibox detector"
    - "rethinking the inception architecture for computer vision"

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)