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: "howto100m learning a text video embedding by watching hundred million narrated video clips"
  Abstract: Learning text-video embeddings usually requires a dataset of video clips with manually provided captions. However, such datasets are expensive and time consuming to create and therefore difficult to obtain on a large scale. In this work, we propose instead to learn such embeddings from video data with readily available natural language annotations in the form of automatically transcribed narrations. The contributions of this work are three-fold. First, we introduce HowTo100M: a large-scale dataset of 136 million video clips sourced from 1.22M narrated instructional web videos depicting humans performing and describing over 23k different visual tasks. Our data collection procedure is fast, scalable and does not require any additional manual annotation. Second, we demonstrate that a text-video embedding trained on this data leads to state-of-the-art results for text-to-video retrieval and action localization on instructional video datasets such as YouCook2 or CrossTask. Finally, we show that this embedding transfers well to other domains: fine-tuning on generic Youtube videos (MSR-VTT dataset) and movies (LSMDC dataset) outperforms models trained on these datasets alone. Our dataset, code and models will be publicly available at: this http URL.

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 "learning spatiotemporal features via video and text pair discrimination"):
  with members:
    - "self supervised spatio temporal representation learning for videos by predicting motion and appearance statistics"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "contrastive multiview coding"
    - "universal dependency parsing from scratch"
    - "dynamonet dynamic action and motion network"
    - "learning correspondence from the cycle consistency of time"
    - "videobert a joint model for video and language representation learning"
    - "video representation learning by dense predictive coding"

Connected through hyperedge HE-3 (induced by source paper "multi modal self supervision from generalized data transformations"):
  with members:
    - "a simple framework for contrastive learning of visual representations"
    - "audiovisual slowfast networks for video recognition"
    - "learning spatiotemporal features via video and text pair discrimination"
    - "on mutual information maximization for representation learning"
    - "self supervised spatio temporal representation learning for videos by predicting motion and appearance statistics"
    - "end to end learning of visual representations from uncurated instructional videos"
    - "learning video representations using contrastive bidirectional transformer"
    - "contrastive multiview coding"

Connected through hyperedge HE-4 (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"
    - "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"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"

Connected through hyperedge HE-5 (induced by source paper "univilm a unified video and language pre training model for multimodal understanding and generation"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "learning video representations using contrastive bidirectional transformer"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "mass masked sequence to sequence pre training for language generation"
    - "a case study on combining asr and visual features for generating instructional video captions"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "multimodal abstractive summarization for how2 videos"
    - "videobert a joint model for video and language representation learning"

Connected through hyperedge HE-6 (induced by source paper "visual grounding in video for unsupervised word translation"):
  with members:
    - "end to end learning of visual representations from uncurated instructional videos"
    - "learning to follow directions in street view"
    - "unsupervised multi modal neural machine translation"
    - "learning visual question answering by bootstrapping hard attention"
    - "lifelong learning for image captioning by asking natural language questions"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-7 (induced by source paper "end to end learning of visual representations from uncurated instructional videos"):
  with members:
    - "a short note on the kinetics 700 human action dataset"
    - "self supervised spatio temporal representation learning for videos by predicting motion and appearance statistics"
    - "learning video representations using contrastive bidirectional transformer"
    - "contrastive multiview coding"
    - "mining youtube a dataset for learning fine grained action concepts from webly supervised video data"
    - "a case study on combining asr and visual features for generating instructional video captions"
    - "toward self supervised object detection in unlabeled videos"
    - "unsupervised pre training of image features on non curated data"

Connected through hyperedge HE-8 (induced by source paper "learning video representations using contrastive bidirectional transformer"):
  with members:
    - "self supervised spatio temporal representation learning for videos by predicting motion and appearance statistics"
    - "lxmert learning cross modality encoder representations from transformers"
    - "contrastive multiview coding"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "learning correspondence from the cycle consistency of time"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "data efficient image recognition with contrastive predictive coding"
    - "bert has a mouth and it must speak bert as a markov random field language model"

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)