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: "don t just listen use your imagination leveraging visual common sense for non visual tasks"
  Abstract: Artificial agents today can answer factual questions. But they fall short on questions that require common sense reasoning. Perhaps this is because most existing common sense databases rely on text to learn and represent knowledge. But much of common sense knowledge is unwritten - partly because it tends not to be interesting enough to talk about, and partly because some common sense is unnatural to articulate in text. While unwritten, it is not unseen. In this paper we leverage semantic common sense knowledge learned from images - i.e. visual common sense - in two textual tasks: fill-in-the-blank and visual paraphrasing. We propose to "imagine" the scene behind the text, and leverage visual cues from the "imagined" scenes in addition to textual cues while answering these questions. We imagine the scenes as a visual abstraction. Our approach outperforms a strong text-only baseline on these tasks. Our proposed tasks can serve as benchmarks to quantitatively evaluate progress in solving tasks that go "beyond recognition". Our code and datasets are publicly available.

Connected through hyperedge HE-1 (induced by source paper "incorporating visual semantics into sentence representations within a grounded space"):
  with members:
    - "sentence embedding alignment for lifelong relation extraction"
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "deep contextualized word representations"
    - "an efficient framework for learning sentence representations"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "learning visually grounded sentence representations"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"

Connected through hyperedge HE-2 (induced by source paper "know2look commonsense knowledge for visual search"):
  with members:
    - "yfcc100m the new data in multimedia research"
    - "very deep convolutional networks for large scale image recognition"
    - "lsda large scale detection through adaptation"
    - "microsoft coco common objects in context"
    - "a multi world approach to question answering about real world scenes based on uncertain input"
    - "show and tell a neural image caption generator"

Connected through hyperedge HE-3 (induced by source paper "seeing voices and hearing faces cross modal biometric matching"):
  with members:
    - "improving speaker turn embedding by crossmodal transfer learning from face embedding"
    - "look listen and learn"
    - "voxceleb a large scale speaker identification dataset"
    - "cross modal scene networks"
    - "ambient sound provides supervision for visual learning"
    - "lip reading sentences in the wild"
    - "action recognition with dynamic image networks"
    - "soundnet learning sound representations from unlabeled video"

Connected through hyperedge HE-4 (induced by source paper "low rank structure of learned representations"):
  with members:
    - "learning structured sparsity in deep neural networks"
    - "adversarial examples in the physical world"
    - "adversarial machine learning at scale"
    - "acdc a structured efficient linear layer"
    - "deep residual learning for image recognition"
    - "deepfool a simple and accurate method to fool deep neural networks"
    - "structured pruning of deep convolutional neural networks"
    - "exploiting linear structure within convolutional networks for efficient evaluation"

Connected through hyperedge HE-5 (induced by source paper "think visually question answering through virtual imagery"):
  with members:
    - "attention is all you need"
    - "cognitive mapping and planning for visual navigation"
    - "generative adversarial text to image synthesis"
    - "tracking the world state with recurrent entity networks"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "hierarchical question image co attention for visual question answering"
    - "dynamic memory networks for visual and textual question answering"
    - "dialog state tracking a machine reading approach using memory network"

Connected through hyperedge HE-6 (induced by source paper "automatic understanding of image and video advertisements"):
  with members:
    - "end to end saliency mapping via probability distribution prediction"
    - "deep reconstruction classification networks for unsupervised domain adaptation"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "what happens if learning to predict the effect of forces in images"
    - "learning aligned cross modal representations from weakly aligned data"
    - "domain separation networks"
    - "unsupervised domain adaptation with residual transfer networks"
    - "whittlesearch interactive image search with relative attribute feedback"

Connected through hyperedge HE-7 (induced by source paper "tgif qa toward spatio temporal reasoning in visual question answering"):
  with members:
    - "image captioning with semantic attention"
    - "multimodal residual learning for visual qa"
    - "end to end concept word detection for video captioning retrieval and question answering"
    - "automatic description generation from images a survey of models datasets and evaluation measures"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "tgif a new dataset and benchmark on animated gif description"
    - "video2gif automatic generation of animated gifs from video"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"

Connected through hyperedge HE-8 (induced by source paper "imagination improves multimodal translation"):
  with members:
    - "doubly attentive decoder for multi modal neural machine translation"
    - "neural machine translation with latent semantic of image and text"
    - "multi task video captioning with video and entailment generation"
    - "a correlational encoder decoder architecture for pivot based sequence generation"
    - "representation of linguistic form and function in recurrent neural networks"
    - "zero resource machine translation by multimodal encoder decoder network with multimedia pivot"
    - "multimodal pivots for image caption translation"
    - "variational neural machine translation"

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)