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: "recurrent memory addressing for describing videos"
  Abstract: Deep Neural Network architectures with external memory components allow the model to perform inference and capture long term dependencies, by storing information explicitly. In this paper, we generalize Key-Value Memory Networks to a multimodal setting, introducing a novel key-addressing mechanism to deal with sequence-to-sequence models. The advantages of the framework are demonstrated on the task of video captioning, i.e generating natural language descriptions for videos. Conditioning on the previous time-step attention distributions for the key-value memory slots, we introduce a temporal structure in the memory addressing schema. The proposed model naturally decomposes the problem of video captioning into vision and language segments, dealing with them as key-value pairs. More specifically, we learn a semantic embedding (v) corresponding to each frame (k) in the video, thereby creating (k, v) memory slots. This allows us to exploit the temporal dependencies at multiple hierarchies (in the recurrent key-addressing; and in the language decoder). Exploiting this flexibility of the framework, we additionally capture spatial dependencies while mapping from the visual to semantic embedding. Extensive experiments on the Youtube2Text dataset demonstrate usefulness of recurrent key-addressing, while achieving competitive scores on BLEU@4, METEOR metrics against state-of-the-art models.

Connected through hyperedge HE-1 (induced by source paper "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"):
  with members:
    - "video description a survey of methods datasets and evaluation metrics"
    - "less is more picking informative frames for video captioning"
    - "reconstruction network for video captioning"
    - "memory augmented attention modelling for videos"
    - "hierarchical boundary aware neural encoder for video captioning"
    - "video captioning with transferred semantic attributes"
    - "semantic compositional networks for visual captioning"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-2 (induced by source paper "improved techniques for gan based facial inpainting"):
  with members:
    - "progressive growing of gans for improved quality stability and variation"
    - "semantically decomposing the latent spaces of generative adversarial networks"
    - "stacked generative adversarial networks"
    - "real time video super resolution with spatio temporal networks and motion compensation"
    - "facenet a unified embedding for face recognition and clustering"
    - "deep learning face attributes in the wild"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "show and tell a neural image caption generator"

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)