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: "attention based multimodal fusion for video description"
  Abstract: Currently successful methods for video description are based on encoder-decoder sentence generation using recur-rent neural networks (RNNs). Recent work has shown the advantage of integrating temporal and/or spatial attention mechanisms into these models, in which the decoder net-work predicts each word in the description by selectively giving more weight to encoded features from specific time frames (temporal attention) or to features from specific spatial regions (spatial attention). In this paper, we propose to expand the attention model to selectively attend not just to specific times or spatial regions, but to specific modalities of input such as image features, motion features, and audio features. Our new modality-dependent attention mechanism, which we call multimodal attention, provides a natural way to fuse multimodal information for video description. We evaluate our method on the Youtube2Text dataset, achieving results that are competitive with current state of the art. More importantly, we demonstrate that our model incorporating multimodal attention as well as temporal attention significantly outperforms the model that uses temporal attention alone.

Connected through hyperedge HE-1 (induced by source paper "move forward and tell a progressive generator of video descriptions"):
  with members:
    - "texygen a benchmarking platform for text generation models"
    - "temporal action detection with structured segment networks"
    - "detecting visual relationships with deep relational networks"
    - "dense captioning events in videos"
    - "contrastive learning for image captioning"
    - "weakly supervised dense video captioning"
    - "hierarchical boundary aware neural encoder for video captioning"
    - "towards diverse and natural image descriptions via a conditional gan"

Connected through hyperedge HE-2 (induced by source paper "sensor transformation attention networks"):
  with members:
    - "lipnet end to end sentence level lipreading"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "end to end attention based large vocabulary speech recognition"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"
    - "recurrent models for auditory attention in multi microphone distance speech recognition"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (induced by source paper "sequential embedding induced text clustering a non parametric bayesian approach"):
  with members:
    - "parallel clustering of single cell transcriptomic data with split merge sampling on dirichlet process mixtures"
    - "towards differentially private truth discovery for crowd sensing systems"
    - "language modeling by clustering with word embeddings for text readability assessment"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "egocentric video description based on temporally linked sequences"):
  with members:
    - "batch based activity recognition from egocentric photo streams"
    - "hierarchical lstm with adjusted temporal attention for video captioning"
    - "learning to remember rare events"
    - "dense captioning events in videos"
    - "toward storytelling from visual lifelogging an overview"
    - "sr clustering semantic regularized clustering for egocentric photo streams segmentation"
    - "semantic summarization of egocentric photo stream events"
    - "video description using bidirectional recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "integrating scene text and visual appearance for fine grained image classification"):
  with members:
    - "text flow a unified text detection system in natural scene images"
    - "image captioning with semantic attention"
    - "textproposals a text specific selective search algorithm for word spotting in the wild"
    - "ssd single shot multibox detector"
    - "weakly supervised fine grained categorization with part based image representation"
    - "synthetic data for text localisation in natural images"
    - "textboxes a fast text detector with a single deep neural network"
    - "deeptext a unified framework for text proposal generation and text detection in natural images"

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)