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: "learning language visual embedding for movie understanding with natural language"
  Abstract: Learning a joint language-visual embedding has a number of very appealing properties and can result in variety of practical application, including natural language image/video annotation and search. In this work, we study three different joint language-visual neural network model architectures. We evaluate our models on large scale LSMDC16 movie dataset for two tasks: 1) Standard Ranking for video annotation and retrieval 2) Our proposed movie multiple-choice test. This test facilitate automatic evaluation of visual-language models for natural language video annotation based on human activities. In addition to original Audio Description (AD) captions, provided as part of LSMDC16, we collected and will make available a) manually generated re-phrasings of those captions obtained using Amazon MTurk b) automatically generated human activity elements in "Predicate + Object" (PO) phrases based on "Knowlywood", an activity knowledge mining model. Our best model archives Recall@10 of 19.2% on annotation and 18.9% on video retrieval tasks for subset of 1000 samples. For multiple-choice test, our best model achieve accuracy 58.11% over whole LSMDC16 public test-set.

Connected through hyperedge HE-1 (induced by source paper "measuring social biases in grounded vision and language embeddings"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "assessing social and intersectional biases in contextualized word representations"
    - "large scale pretraining for visual dialog a simple state of the art baseline"
    - "iterative answer prediction with pointer augmented multimodal transformers for textvqa"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "visualbert a simple and performant baseline for vision and language"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "howto100m learning a text video embedding by watching hundred million narrated video clips"):
  with members:
    - "cross task weakly supervised learning from instructional videos"
    - "coin a large scale dataset for comprehensive instructional video analysis"
    - "scaling egocentric vision the epic kitchens dataset"
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "unsupervised learning and segmentation of complex activities from video"
    - "learning to compose topic aware mixture of experts for zero shot video captioning"
    - "a joint sequence fusion model for video question answering and retrieval"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-3 (induced by source paper "neural sequential phrase grounding seqground"):
  with members:
    - "iterative visual reasoning beyond convolutions"
    - "a neural multi sequence alignment technique neumatch"
    - "graph r cnn for scene graph generation"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "mattnet modular attention network for referring expression comprehension"
    - "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"
    - "discriminative bimodal networks for visual localization and detection with natural language queries"
    - "query guided regression network with context policy for phrase grounding"

Connected through hyperedge HE-4 (induced by source paper "use what you have video retrieval using representations from collaborative experts"):
  with members:
    - "end to end learning of visual representations from uncurated instructional videos"
    - "cross task weakly supervised learning from instructional videos"
    - "on the variance of the adaptive learning rate and beyond"
    - "videobert a joint model for video and language representation learning"
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "massively parallel hyperparameter tuning"
    - "a joint sequence fusion model for video question answering and retrieval"
    - "learnable pins cross modal embeddings for person identity"

Connected through hyperedge HE-5 (induced by source paper "fine grained action retrieval through multiple parts of speech embeddings"):
  with members:
    - "action2vec a crossmodal embedding approach to action learning"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"
    - "higher order network for action recognition"
    - "scaling egocentric vision the epic kitchens dataset"
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "a joint sequence fusion model for video question answering and retrieval"
    - "on the effectiveness of task granularity for transfer learning"
    - "cross modal embeddings for video and audio retrieval"

Connected through hyperedge HE-6 (induced by source paper "multimodal abstractive summarization for how2 videos"):
  with members:
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "how2 a large scale dataset for multimodal language understanding"
    - "product title refinement via multi modal generative adversarial learning"
    - "nmtpy a flexible toolkit for advanced neural machine translation systems"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "get to the point summarization with pointer generator networks"
    - "attention strategies for multi source sequence to sequence learning"

Connected through hyperedge HE-7 (induced by source paper "are we asking the right questions in movieqa"):
  with members:
    - "comparing attention based convolutional and recurrent neural networks success and limitations in machine reading comprehension"
    - "speaker naming in movies"
    - "multimodal dual attention memory for video story question answering"
    - "from trailers to storylines an efficient way to learn from movies"
    - "tvqa localized compositional video question answering"
    - "movie question answering remembering the textual cues for layered visual contents"
    - "generating descriptions with grounded and co referenced people"
    - "a read write memory network for movie story understanding"

Connected through hyperedge HE-8 (induced by source paper "learning a text video embedding from incomplete and heterogeneous data"):
  with members:
    - "a joint sequence fusion model for video question answering and retrieval"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning two branch neural networks for image text matching tasks"
    - "deep image prior"
    - "learnable pooling with context gating for video classification"
    - "look listen and learn"
    - "learning from video and text via large scale discriminative clustering"
    - "localizing moments in video with natural language"

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)