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: "deepstory video story qa by deep embedded memory networks"
  Abstract: Question-answering (QA) on video contents is a significant challenge for achieving human-level intelligence as it involves both vision and language in real-world settings. Here we demonstrate the possibility of an AI agent performing video story QA by learning from a large amount of cartoon videos. We develop a video-story learning model, i.e. Deep Embedded Memory Networks (DEMN), to reconstruct stories from a joint scene-dialogue video stream using a latent embedding space of observed data. The video stories are stored in a long-term memory component. For a given question, an LSTM-based attention model uses the long-term memory to recall the best question-story-answer triplet by focusing on specific words containing key information. We trained the DEMN on a novel QA dataset of children's cartoon video series, Pororo. The dataset contains 16,066 scene-dialogue pairs of 20.5-hour videos, 27,328 fine-grained sentences for scene description, and 8,913 story-related QA pairs. Our experimental results show that the DEMN outperforms other QA models. This is mainly due to 1) the reconstruction of video stories in a scene-dialogue combined form that utilize the latent embedding and 2) attention. DEMN also achieved state-of-the-art results on the MovieQA benchmark.

Connected through hyperedge HE-1 (induced by source paper "a 3 accelerating attention mechanisms in neural networks with approximation"):
  with members:
    - "regularized evolution for image classifier architecture search"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "on the dimensionality of word embedding"
    - "in datacenter performance analysis of a tensor processing unit"
    - "learning transferable architectures for scalable image recognition"
    - "attention is all you need"
    - "multilingual hierarchical attention networks for document classification"
    - "a read write memory network for movie story understanding"

Connected through hyperedge HE-2 (induced by source paper "tvr a large scale dataset for video subtitle moment retrieval"):
  with members:
    - "tvqa spatio temporal grounding for video question answering"
    - "vatex a large scale high quality multilingual dataset for video and language research"
    - "temporal localization of moments in video collections with natural language"
    - "roberta a robustly optimized bert pretraining approach"
    - "excl extractive clip localization using natural language descriptions"
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "mattnet modular attention network for referring expression comprehension"

Connected through hyperedge HE-3 (induced by source paper "multi step joint modality attention network for scene aware dialogue system"):
  with members:
    - "dual attention networks for visual reference resolution in visual dialog"
    - "multi step reasoning via recurrent dual attention for visual dialog"
    - "bilinear attention networks"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "tvqa localized compositional video question answering"
    - "recursive visual attention in visual dialog"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "video question answering via attribute augmented attention network learning"

Connected through hyperedge HE-4 (induced by source paper "focal visual text attention for memex question answering"):
  with members:
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "differential attention for visual question answering"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "ivqa inverse visual question answering"
    - "an analysis of visual question answering algorithms"

Connected through hyperedge HE-5 (induced by source paper "learning question guided video representation for multi turn video question answering"):
  with members:
    - "heterogeneous memory enhanced multimodal attention model for video question answering"
    - "entropy enhanced multimodal attention model for scene aware dialogue generation"
    - "reactive multi stage feature fusion for multimodal dialogue modeling"
    - "audio visual scene aware dialog"
    - "motion appearance co memory networks for video question answering"
    - "context attention and audio feature explorations for audio visual scene aware dialog"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"

Connected through hyperedge HE-6 (induced by source paper "tvqa spatio temporal grounding for video question answering"):
  with members:
    - "neural baby talk"
    - "know what you don t know unanswerable questions for squad"
    - "mattnet modular attention network for referring expression comprehension"
    - "grounded video description"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "tvqa localized compositional video question answering"
    - "bsn boundary sensitive network for temporal action proposal generation"

Connected through hyperedge HE-7 (induced by source paper "video question generation via cross modal self attention networks learning"):
  with members:
    - "lifelong learning for image captioning by asking natural language questions"
    - "end to end dense video captioning with masked transformer"
    - "tvqa localized compositional video question answering"
    - "from film to video multi turn question answering with multi modal context"
    - "learning to ask neural question generation for reading comprehension"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "visual question generation as dual task of visual question answering"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "learning to reason with relational video representation for question answering"):
  with members:
    - "learning regularity in skeleton trajectories for anomaly detection in videos"
    - "progressive attention memory network for movie story question answering"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "motion appearance co memory networks for video question answering"
    - "multimodal dual attention memory for video story question answering"
    - "compositional attention networks for machine reasoning"
    - "variational memory encoder decoder"
    - "tvqa localized compositional video question answering"

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)