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: "translating videos to natural language using deep recurrent neural networks"
  Abstract: Solving the visual symbol grounding problem has long been a goal of artificial intelligence. The field appears to be advancing closer to this goal with recent breakthroughs in deep learning for natural language grounding in static images. In this paper, we propose to translate videos directly to sentences using a unified deep neural network with both convolutional and recurrent structure. Described video datasets are scarce, and most existing methods have been applied to toy domains with a small vocabulary of possible words. By transferring knowledge from 1.2M+ images with category labels and 100,000+ images with captions, our method is able to create sentence descriptions of open-domain videos with large vocabularies. We compare our approach with recent work using language generation metrics, subject, verb, and object prediction accuracy, and a human evaluation.

Connected through hyperedge HE-1 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning"):
  with members:
    - "end to end learning of visual representations from uncurated instructional videos"
    - "use what you have video retrieval using representations from collaborative experts"
    - "polysemous visual semantic embedding for cross modal retrieval"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "videobert a joint model for video and language representation learning"
    - "grounding object detections with transcriptions"
    - "heterogeneous memory enhanced multimodal attention model for video question answering"
    - "learning to detect and retrieve objects from unlabeled videos"

Connected through hyperedge HE-2 (induced by source paper "multi modal dense video captioning"):
  with members:
    - "watch listen and tell multi modal weakly supervised dense event captioning"
    - "memory attended recurrent network for video captioning"
    - "a case study on combining asr and visual features for generating instructional video captions"
    - "streamlined dense video captioning"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "weakly supervised dense event captioning in videos"
    - "move forward and tell a progressive generator of video descriptions"

Connected through hyperedge HE-3 (induced by source paper "spatio temporal ranked attention networks for video captioning"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "object aware aggregation with bidirectional temporal graph for video captioning"
    - "less is more picking informative frames for video captioning"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "the sound of pixels"
    - "improved image captioning via policy gradient optimization of spider"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-4 (induced by source paper "video storytelling textual summaries for events"):
  with members:
    - "ffnet video fast forwarding via reinforcement learning"
    - "predicting visual features from text for image and video caption retrieval"
    - "unsupervised learning of view invariant action representations"
    - "query adaptive video summarization via quality aware relevance estimation"
    - "improved image captioning via policy gradient optimization of spider"
    - "dual glance model for deciphering social relationships"
    - "summarization of user generated sports video by using deep action recognition features"
    - "attention transfer from web images for video recognition"

Connected through hyperedge HE-5 (induced by source paper "clara clinical report auto completion"):
  with members:
    - "clinically accurate chest x ray report generation"
    - "knowledge driven encode retrieve paraphrase for medical image report generation"
    - "neural baby talk"
    - "a neural compositional paradigm for image captioning"
    - "personalized language model for query auto completion"
    - "on the automatic generation of medical imaging reports"
    - "hybrid retrieval generation reinforced agent for medical image report generation"
    - "producing radiologist quality reports for interpretable artificial intelligence"

Connected through hyperedge HE-6 (induced by source paper "object relational graph with teacher recommended learning for video captioning"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "controllable video captioning with pos sequence guidance based on gated fusion network"
    - "memory attended recurrent network for video captioning"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "object aware aggregation with bidirectional temporal graph for video captioning"
    - "relation aware graph attention network for visual question answering"
    - "less is more picking informative frames for video captioning"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "delving deeper into the decoder for video captioning"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "a semantics assisted video captioning model trained with scheduled sampling"
    - "a hierarchical reinforced sequence operation method for unsupervised text style transfer"
    - "eco efficient convolutional network for online video understanding"
    - "learning to compose topic aware mixture of experts for zero shot video captioning"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "reinforced video captioning with entailment rewards"
    - "attend and interact higher order object interactions for video understanding"

Connected through hyperedge HE-8 (induced by source paper "hierarchical memory decoding for video captioning"):
  with members:
    - "less is more picking informative frames for video captioning"
    - "middle out decoding"
    - "reconstruction network for video captioning"
    - "attention is all you need"
    - "hierarchical lstm with adjusted temporal attention for video captioning"
    - "convolutional image captioning"
    - "consensus based sequence training for video captioning"
    - "convolutional sequence to sequence learning"

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)