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"
  Abstract: The wide distribution of digital devices as well as cheap storage allow us to take series of photos making sure not to miss any specific beautiful moment. Thereby, the huge and constantly growing image assembly makes it quite time-consuming to manually pick the best shots afterwards. Even more challenging, finding the most aesthetically pleasing images that might also be worth sharing is a largely subjective task in which general rules rarely apply. Nowadays, online platforms allow users to "like" or favor certain content with a single click. As we aim to predict the aesthetic quality of images, we now make use of such multi-user agreements. More precisely, we assemble a large data set of 380K images with associated meta information and derive a score to rate how visually pleasing a given photo is. predict the aesthetic quality of any arbitrary image or video, we transfer the Our proposed model of aesthetics is validated in a user study. 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 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"

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)