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: "how to make an image more memorable a deep style transfer approach"
  Abstract: Recent works have shown that it is possible to automatically predict intrinsic image properties like memorability. In this paper, we take a step forward addressing the question: "Can we make an image more memorable?". Methods for automatically increasing image memorability would have an impact in many application fields like education, gaming or advertising. Our work is inspired by the popular editing-by-applying-filters paradigm adopted in photo editing applications, like Instagram and Prisma. In this context, the problem of increasing image memorability maps to that of retrieving "memorabilizing" filters or style "seeds". Still, users generally have to go through most of the available filters before finding the desired solution, thus turning the editing process into a resource and time consuming task. In this work, we show that it is possible to automatically retrieve the best style seeds for a given image, thus remarkably reducing the number of human attempts needed to find a good match. Our approach leverages from recent advances in the field of image synthesis and adopts a deep architecture for generating a memorable picture from a given input image and a style seed. Importantly, to automatically select the best style a novel learning-based solution, also relying on deep models, is proposed. Our experimental evaluation, conducted on publicly available benchmarks, demonstrates the effectiveness of the proposed approach for generating memorable images through automatic style seed selection

Connected through hyperedge HE-1 (induced by source paper "ganalyze toward visual definitions of cognitive image properties"):
  with members:
    - "large scale gan training for high fidelity natural image synthesis"
    - "changing the image memorability from basic photo editing to gans"
    - "a style based generator architecture for generative adversarial networks"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "network dissection quantifying interpretability of deep visual representations"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "interpreting deep visual representations via network dissection"

Connected through hyperedge HE-2 (induced by source paper "changing the image memorability from basic photo editing to gans"):
  with members:
    - "video to video synthesis"
    - "amnet memorability estimation with attention"
    - "attgan facial attribute editing by only changing what you want"
    - "progressive growing of gans for improved quality stability and variation"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "show and recall learning what makes videos memorable"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "deepgum learning deep robust regression with a gaussian uniform mixture model"):
  with members:
    - "a comprehensive analysis of deep regression"
    - "viraliency pooling local virality"
    - "robust head pose estimation based on partially latent mixture of linear regressions"
    - "learning from noisy labels with distillation"
    - "em algorithms for weighted data clustering with application to audio visual scene analysis"
    - "fashion landmark detection in the wild"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "robust optimization for deep regression"

Connected through hyperedge HE-4 (induced by source paper "viraliency pooling local virality"):
  with members:
    - "em algorithms for weighted data clustering with application to audio visual scene analysis"
    - "are safer looking neighborhoods more lively a multimodal investigation into urban life"
    - "deep learning the city quantifying urban perception at a global scale"
    - "understanding image virality"
    - "a neural algorithm of artistic style"
    - "learning deep features for discriminative localization"
    - "very deep convolutional networks for large scale image recognition"

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)