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: "hierarchical boundary aware neural encoder for video captioning"
  Abstract: The use of Recurrent Neural Networks for video captioning has recently gained a lot of attention, since they can be used both to encode the input video and to generate the corresponding description. In this paper, we present a recurrent video encoding scheme which can discover and leverage the hierarchical structure of the video. Unlike the classical encoder-decoder approach, in which a video is encoded continuously by a recurrent layer, we propose a novel LSTM cell which can identify discontinuity points between frames or segments and modify the temporal connections of the encoding layer accordingly. We evaluate our approach on three large-scale datasets: the Montreal Video Annotation dataset, the MPII Movie Description dataset and the Microsoft Video Description Corpus. Experiments show that our approach can discover appropriate hierarchical representations of input videos and improve the state of the art results on movie description datasets.

Connected through hyperedge HE-1 (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-2 (induced by source paper "hierarchical conditional relation networks for video question answering"):
  with members:
    - "learning to reason with relational video representation for question answering"
    - "progressive attention memory network for movie story question answering"
    - "heterogeneous memory enhanced multimodal attention model for video question answering"
    - "long term feature banks for detailed video understanding"
    - "bilinear attention networks"
    - "non local netvlad encoding for video classification"
    - "hierarchical video frame sequence representation with deep convolutional graph network"
    - "motion appearance co memory networks for video question answering"

Connected through hyperedge HE-3 (induced by source paper "multi modal music information retrieval augmenting audio analysis with visual computing for improved music video analysis"):
  with members:
    - "predicting time to failure of plasma etching equipment using machine learning"
    - "cross modal music retrieval and applications an overview of key methodologies"
    - "multi task music representation learning from multi label embeddings"
    - "fashion and apparel classification using convolutional neural networks"
    - "a multi modal deep neural network approach to bird song identification"
    - "large scale audio visual video analytics platform for forensic investigations of terroristic attacks"
    - "multi temporal resolution convolutional neural networks for acoustic scene classification"
    - "content based video music retrieval using soft intra modal structure constraint"

Connected through hyperedge HE-4 (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"

Connected through hyperedge HE-5 (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"
    - "video captioning with transferred semantic attributes"
    - "recurrent memory addressing for describing videos"
    - "semantic compositional networks for visual captioning"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-6 (induced by source paper "watch it twice video captioning with a refocused video encoder"):
  with members:
    - "unpaired image captioning via scene graph alignments"
    - "scene graph generation with external knowledge and image reconstruction"
    - "less is more picking informative frames for video captioning"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "reconstruction network for video captioning"
    - "unpaired image captioning by language pivoting"
    - "end to end video captioning with multitask reinforcement learning"
    - "exploring visual relationship for image captioning"

Connected through hyperedge HE-7 (induced by source paper "streamlined dense video captioning"):
  with members:
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "move forward and tell a progressive generator of video descriptions"
    - "end to end dense video captioning with masked transformer"
    - "jointly localizing and describing events for dense video captioning"
    - "reconstruction network for video captioning"
    - "the activitynet large scale activity recognition challenge 2018 summary"
    - "attention is all you need"
    - "deep reinforcement learning based image captioning with embedding reward"

Connected through hyperedge HE-8 (induced by source paper "temporal deformable convolutional encoder decoder networks for video captioning"):
  with members:
    - "less is more picking informative frames for video captioning"
    - "jointly localizing and describing events for dense video captioning"
    - "exploring visual relationship for image captioning"
    - "hierarchical lstm with adjusted temporal attention for video captioning"
    - "incorporating copying mechanism in image captioning for learning novel objects"
    - "integrating both visual and audio cues for enhanced video caption"
    - "convolutional sequence to sequence learning"
    - "video captioning with guidance of multimodal latent topics"

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)