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: "video description using bidirectional recurrent neural networks"
  Abstract: Although traditionally used in the machine translation field, the encoder-decoder framework has been recently applied for the generation of video and image descriptions. The combination of Convolutional and Recurrent Neural Networks in these models has proven to outperform the previous state of the art, obtaining more accurate video descriptions. In this work we propose pushing further this model by introducing two contributions into the encoding stage. First, producing richer image representations by combining object and location information from Convolutional Neural Networks and second, introducing Bidirectional Recurrent Neural Networks for capturing both forward and backward temporal relationships in the input frames.

Connected through hyperedge HE-1 (induced by source paper "interactive predictive neural multimodal systems"):
  with members:
    - "direct speech to speech translation with a sequence to sequence model"
    - "online learning for effort reduction in interactive neural machine translation"
    - "nmt keras a very flexible toolkit with a focus on interactive nmt and online learning"
    - "egocentric video description based on temporally linked sequences"
    - "learning transferable architectures for scalable image recognition"
    - "attention is all you need"
    - "six challenges for neural machine translation"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-2 (induced by source paper "online learning for effort reduction in interactive neural machine translation"):
  with members:
    - "a reinforcement learning approach to interactive predictive neural machine translation"
    - "active learning for interactive neural machine translation of data streams"
    - "nmt keras a very flexible toolkit with a focus on interactive nmt and online learning"
    - "online learning for neural machine translation post editing"
    - "attention is all you need"
    - "lexically constrained decoding for sequence generation using grid beam search"
    - "trainable greedy decoding for neural machine translation"
    - "massive exploration of neural machine translation architectures"

Connected through hyperedge HE-3 (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"
    - "attention based multimodal fusion for video description"

Connected through hyperedge HE-4 (induced by source paper "storytelling of photo stream with bidirectional multi thread recurrent neural network"):
  with members:
    - "contextual lstm clstm models for large scale nlp tasks"
    - "sequence to sequence video to text"
    - "guiding long short term memory for image caption generation"
    - "order embeddings of images and language"
    - "very deep convolutional networks for large scale image recognition"
    - "deep captioning with multimodal recurrent neural networks m rnn"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "translating videos to natural language using deep recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "vibiknet visual bidirectional kernelized network for visual question answering"):
  with members:
    - "dual attention networks for multimodal reasoning and matching"
    - "multimodal residual learning for visual qa"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "kernelized deep convolutional neural network for describing complex images"
    - "vqa visual question answering"
    - "show attend and tell neural image caption generation with visual attention"
    - "microsoft coco captions data collection and evaluation server"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "short term traffic flow forecasting with spatial temporal correlation in a hybrid deep learning framework"):
  with members:
    - "deep convolutional networks on graph structured data"
    - "deep learning with s shaped rectified linear activation units"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "modeling spatial temporal clues in a hybrid deep learning framework for video classification"
    - "adam a method for stochastic optimization"
    - "striving for simplicity the all convolutional net"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "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)