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 captioning with recurrent networks based on frame and video level features and visual content classification"
  Abstract: In this paper, we describe the system for generating textual descriptions of short video clips using recurrent neural networks (RNN), which we used while participating in the Large Scale Movie Description Challenge 2015 in ICCV 2015. Our work builds on static image captioning systems with RNN based language models and extends this framework to videos utilizing both static image features and video-specific features. In addition, we study the usefulness of visual content classifiers as a source of additional information for caption generation. With experimental results we show that utilizing keyframe based features, dense trajectory video features and content classifier outputs together gives better performance than any one of them individually.

Connected through hyperedge HE-1 (induced by source paper "a review of modularization techniques in artificial neural networks"):
  with members:
    - "knowledge transfer in deep block modular neural networks"
    - "graph hypernetworks for neural architecture search"
    - "mattnet modular attention network for referring expression comprehension"
    - "relational inductive biases deep learning and graph networks"
    - "a graph to sequence model for amr to text generation"
    - "dense and diverse capsule networks making the capsules learn better"
    - "evolving deep neural networks"
    - "hierarchical semantic segmentation using modular convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "audio visual scene aware dialog"):
  with members:
    - "tvqa localized compositional video question answering"
    - "the activitynet large scale activity recognition challenge 2018 summary"
    - "a dataset for document grounded conversations"
    - "two can play this game visual dialog with discriminative question generation and answering"
    - "evaluating visual conversational agents via cooperative human ai games"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "dense captioning events in videos"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-3 (induced by source paper "video captioning with transferred semantic attributes"):
  with members:
    - "image captioning with semantic attention"
    - "rich image captioning in the wild"
    - "boosting image captioning with attributes"
    - "jointly modeling embedding and translation to bridge video and language"
    - "what value do explicit high level concepts have in vision to language problems"
    - "delving deeper into convolutional networks for learning video representations"
    - "the long short story of movie description"
    - "a dataset for movie description"

Connected through hyperedge HE-4 (induced by source paper "temporal tessellation a unified approach for video analysis"):
  with members:
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "dense correspondences across scenes and scales"
    - "summary transfer exemplar based subset selection for video summarization"
    - "end to end concept word detection for video captioning retrieval and question answering"
    - "video summarization with long short term memory"
    - "end to end learning of action detection from frame glimpses in videos"
    - "rnn fisher vectors for action recognition and image annotation"
    - "unsupervised learning of video representations using lstms"

Connected through hyperedge HE-5 (induced by source paper "beyond caption to narrative video captioning with multiple sentences"):
  with members:
    - "jointly modeling embedding and translation to bridge video and language"
    - "a dataset for movie description"
    - "using descriptive video services to create a large data source for video annotation research"
    - "learning to track for spatio temporal action localization"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"
    - "action recognition with trajectory pooled deep convolutional descriptors"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "frame and segment level features and candidate pool evaluation for video caption generation"):
  with members:
    - "a dataset for movie description"
    - "deep residual learning for image recognition"
    - "using descriptive video services to create a large data source for video annotation research"
    - "sequence to sequence video to text"
    - "going deeper with convolutions"
    - "cider consensus based image description evaluation"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "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)