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: "videobert a joint model for video and language representation learning"
  Abstract: Self-supervised learning has become increasingly important to leverage the abundance of unlabeled data available on platforms like YouTube. Whereas most existing approaches learn low-level representations, we propose a joint visual-linguistic model to learn high-level features without any explicit supervision. In particular, inspired by its recent success in language modeling, we build upon the BERT model to learn bidirectional joint distributions over sequences of visual and linguistic tokens, derived from vector quantization of video data and off-the-shelf speech recognition outputs, respectively. We use this model in a number of tasks, including action classification and video captioning. We show that it can be applied directly to open-vocabulary classification, and confirm that large amounts of training data and cross-modal information are critical to performance. Furthermore, we outperform the state-of-the-art on video captioning, and quantitative results verify that the model learns high-level semantic features.

Connected through hyperedge HE-1 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations"):
  with members:
    - "deep bayesian network for visual question generation"
    - "incorporating visual semantics into sentence representations within a grounded space"
    - "visualizing and measuring the geometry of bert"
    - "lxmert learning cross modality encoder representations from transformers"
    - "bert rediscovers the classical nlp pipeline"
    - "context aware zero shot learning for object recognition"
    - "fusion of detected objects in text for visual question answering"
    - "supervised multimodal bitransformers for classifying images and text"

Connected through hyperedge HE-2 (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"
    - "grounding object detections with transcriptions"
    - "heterogeneous memory enhanced multimodal attention model for video question answering"
    - "learning to detect and retrieve objects from unlabeled videos"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"

Connected through hyperedge HE-3 (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"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"
    - "video representation learning by dense predictive coding"

Connected through hyperedge HE-4 (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"
    - "visualbert a simple and performant baseline for vision and language"

Connected through hyperedge HE-5 (induced by source paper "towards learning a generic agent for vision and language navigation via pre training"):
  with members:
    - "vision and dialog navigation"
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "lxmert learning cross modality encoder representations from transformers"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "robust navigation with language pretraining and stochastic sampling"
    - "vl bert pre training of generic visual linguistic representations"
    - "the regretful agent heuristic aided navigation through progress estimation"

Connected through hyperedge HE-6 (induced by source paper "xgpt cross modal generative pre training for image captioning"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "uniter learning universal image text representations"
    - "fusion of detected objects in text for visual question answering"
    - "unicoder a universal language encoder by pre training with multiple cross lingual tasks"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "attention on attention for image captioning"
    - "mass masked sequence to sequence pre training for language generation"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"

Connected through hyperedge HE-7 (induced by source paper "codebert a pre trained model for programming and natural languages"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "language models as knowledge bases"
    - "pre trained contextual embedding of source code"
    - "codesearchnet challenge evaluating the state of semantic code search"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "olmpics on what language model pre training captures"
    - "how multilingual is multilingual bert"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-8 (induced by source paper "cross modal learning for multi modal video categorization"):
  with members:
    - "learning relationships between text audio and video via deep canonical correlation for multimodal language analysis"
    - "bert for large scale video segment classification with test time augmentation"
    - "multimodal fusion with deep neural networks for audio video emotion recognition"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "fully automatic video colorization with self regularization and diversity"
    - "making sense of vision and touch learning multimodal representations for contact rich tasks"
    - "multimodal affective analysis using hierarchical attention strategy with word level alignment"
    - "cooperative learning of audio and video models from self supervised synchronization"

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)