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: "amnet memorability estimation with attention"
  Abstract: In this paper we present the design and evaluation of an end-to-end trainable, deep neural network with a visual attention mechanism for memorability estimation in still images. We analyze the suitability of transfer learning of deep models from image classification to the memorability task. Further on we study the impact of the attention mechanism on the memorability estimation and evaluate our network on the SUN Memorability and the LaMem datasets. Our network outperforms the existing state of the art models on both datasets in terms of the Spearman's rank correlation as well as the mean squared error, closely matching human consistency.

Connected through hyperedge HE-1 (induced by source paper "predicting visual memory schemas with variational autoencoders"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "defining image memorability using the visual memory schema"
    - "on the behavior of convolutional nets for feature extraction"
    - "do semantic parts emerge in convolutional neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "object detectors emerge in deep scene cnns"

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"
    - "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"
    - "how to make an image more memorable a deep style transfer approach"
    - "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 "summarizing videos with attention"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "learning video summarization using unpaired data"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "attention is all you need"
    - "video summarization with attention based encoder decoder networks"
    - "convolutional sequence to sequence learning"
    - "a structured self attentive sentence embedding"
    - "reflections on shannon information in search of a natural information entropy for images"

Connected through hyperedge HE-4 (induced by source paper "videomem constructing analyzing predicting short term and long term video memorability"):
  with members:
    - "sodeep a sorting deep net to learn ranking loss surrogates"
    - "mediaeval 2018 predicting media memorability task"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "show and recall learning what makes videos memorable"
    - "deep residual learning for image recognition"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "mediaeval 2018 predicting media memorability task"):
  with members:
    - "show and recall learning what makes videos memorable"
    - "rethinking the inception architecture for computer vision"
    - "learning spatiotemporal features with 3d convolutional networks"

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.AI(Artificial Intelligence)