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 guidance of multimodal latent topics"
  Abstract: The topic diversity of open-domain videos leads to various vocabularies and linguistic expressions in describing video contents, and therefore, makes the video captioning task even more challenging. In this paper, we propose an unified caption framework, M&M TGM, which mines multimodal topics in unsupervised fashion from data and guides the caption decoder with these topics. Compared to pre-defined topics, the mined multimodal topics are more semantically and visually coherent and can reflect the topic distribution of videos better. We formulate the topic-aware caption generation as a multi-task learning problem, in which we add a parallel task, topic prediction, in addition to the caption task. For the topic prediction task, we use the mined topics as the teacher to train a student topic prediction model, which learns to predict the latent topics from multimodal contents of videos. The topic prediction provides intermediate supervision to the learning process. As for the caption task, we propose a novel topic-aware decoder to generate more accurate and detailed video descriptions with the guidance from latent topics. The entire learning procedure is end-to-end and it optimizes both tasks simultaneously. The results from extensive experiments conducted on the MSR-VTT and Youtube2Text datasets demonstrate the effectiveness of our proposed model. M&M TGM not only outperforms prior state-of-the-art methods on multiple evaluation metrics and on both benchmark datasets, but also achieves better generalization ability.

Connected through hyperedge HE-1 (induced by source paper "better captioning with sequence level exploration"):
  with members:
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "exploring visual relationship for image captioning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "show adapt and tell adversarial training of cross domain image captioner"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "diverse and accurate image description using a variational auto encoder with an additive gaussian encoding space"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "video captioning with transferred semantic attributes"

Connected through hyperedge HE-2 (induced by source paper "controllable video captioning with pos sequence guidance based on gated fusion network"):
  with members:
    - "reconstruct and represent video contents for captioning via reinforcement learning"
    - "hierarchical photo scene encoder for album storytelling"
    - "less is more picking informative frames for video captioning"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "unsupervised image captioning"
    - "reconstruction network for video captioning"
    - "recurrent fusion network for image captioning"

Connected through hyperedge HE-3 (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"
    - "hierarchical boundary aware neural encoder for video captioning"

Connected through hyperedge HE-4 (induced by source paper "low rank hoca efficient high order cross modal attention for video captioning"):
  with members:
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "efficient low rank multimodal fusion with modality specific factors"
    - "reconstruction network for video captioning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "video captioning via hierarchical reinforcement learning"
    - "weakly supervised dense video captioning"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"
    - "video captioning with transferred semantic attributes"

Connected through hyperedge HE-5 (induced by source paper "object aware aggregation with bidirectional temporal graph for video captioning"):
  with members:
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "reconstruction network for video captioning"
    - "hierarchical boundary aware neural encoder for video captioning"
    - "bidirectional multirate reconstruction for temporal modeling in videos"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "hierarchical recurrent neural encoder for video representation with application to captioning"
    - "deep residual learning for image recognition"
    - "sequence to sequence video to text"

Connected through hyperedge HE-6 (induced by source paper "time sync video tag extraction using semantic association graph"):
  with members:
    - "analogical reasoning on chinese morphological and semantic relations"
    - "permanence and community structure in complex networks"
    - "world knowledge as indirect supervision for document clustering"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "an attempt towards interpretable audio visual video captioning"):
  with members:
    - "less is more picking informative frames for video captioning"
    - "learning to localize sound source in visual scenes"
    - "pervasive attention 2d convolutional neural networks for sequence to sequence prediction"
    - "audio visual event localization in unconstrained videos"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "learning to separate object sounds by watching unlabeled video"
    - "reconstruction network for video captioning"
    - "the sound of pixels"

Connected through hyperedge HE-8 (induced by source paper "mining for meaning from vision to language through multiple networks consensus"):
  with members:
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "reinforced video captioning with entailment rewards"
    - "improved image captioning via policy gradient optimization of spider"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "video captioning via hierarchical reinforcement learning"
    - "the kinetics human action video dataset"
    - "weakly supervised dense video captioning"
    - "multi task video captioning with video and entailment generation"

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)