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: "inferring the why in images"
  Abstract: Abstract : Humans have the remarkable capability to infer the motivations of other people's actions, likely due to cognitive skills known in psychophysics as the theory of mind. In this paper, we strive to build a computational model that predicts the motivation behind the actions of people from images. To our knowledge, this challenging problem has not yet been extensively explored in computer vision. We present a novel learning based framework that uses high-level visual recognition to infer why people are performing an actions in images. However, the information in an image alone may not be sufficient to automatically solve this task. Since humans can rely on their own experiences to infer motivation, we propose to give computer vision systems access to some of these experiences by using recently developed natural language models to mine knowledge stored in massive amounts of text. While we are still far away from automatically inferring motivation, our results suggest that transferring knowledge from language into vision can help machines understand why a person might be performing an action in an image.

Connected through hyperedge HE-1 (induced by source paper "video2commonsense generating commonsense descriptions to enrich video captioning"):
  with members:
    - "vqa lol visual question answering under the lens of logic"
    - "procedure planning in instructional videos"
    - "lxmert learning cross modality encoder representations from transformers"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "blocksworld revisited learning and reasoning to generate event sequences from image pairs"
    - "roberta a robustly optimized bert pretraining approach"
    - "from recognition to cognition visual commonsense reasoning"

Connected through hyperedge HE-2 (induced by source paper "asynchronous temporal fields for action recognition"):
  with members:
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "learning visual storylines with skipping recurrent neural networks"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "towards weakly supervised action localization"
    - "spot on action localization from pointly supervised proposals"
    - "sympathy for the details dense trajectories and hybrid classification architectures for action recognition"
    - "end to end learning of action detection from frame glimpses in videos"
    - "unsupervised learning of video representations using lstms"

Connected through hyperedge HE-3 (induced by source paper "leveraging visual question answering for image caption ranking"):
  with members:
    - "question relevance in vqa identifying non visual and false premise questions"
    - "exploring models and data for image question answering"
    - "unsupervised visual representation learning by context prediction"
    - "multimodal convolutional neural networks for matching image and sentence"
    - "don t just listen use your imagination leveraging visual common sense for non visual tasks"
    - "vqa visual question answering"
    - "visual madlibs fill in the blank image generation and question answering"
    - "simple baseline for visual question answering"

Connected through hyperedge HE-4 (induced by source paper "prediction of manipulation actions"):
  with members:
    - "dynamics based 3d skeletal hand tracking"
    - "segmental spatiotemporal cnns for fine grained action segmentation"
    - "beyond short snippets deep networks for video classification"
    - "recurrent network models for human dynamics"
    - "renet a recurrent neural network based alternative to convolutional networks"
    - "anticipating visual representations from unlabeled video"
    - "very deep convolutional networks for large scale image recognition"
    - "long term recurrent convolutional networks for visual recognition and description"

Connected through hyperedge HE-5 (induced by source paper "annotation methodologies for vision and language dataset creation"):
  with members:
    - "unsupervised visual sense disambiguation for verbs using multimodal embeddings"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "embracing error to enable rapid crowdsourcing"
    - "describing common human visual actions in images"
    - "vqa visual question answering"
    - "a survey of current datasets for vision and language research"
    - "visual semantic role labeling"
    - "visual word2vec vis w2v learning visually grounded word embeddings using abstract scenes"

Connected through hyperedge HE-6 (induced by source paper "subjects and their objects localizing interactees for a person centric view of importance"):
  with members:
    - "describing common human visual actions in images"
    - "visual semantic role labeling"
    - "exploring nearest neighbor approaches for image captioning"
    - "how transferable are features in deep neural networks"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "caffe convolutional architecture for fast feature embedding"
    - "from captions to visual concepts and back"
    - "deep visual semantic alignments for generating image descriptions"

Connected through hyperedge HE-7 (induced by source paper "visual7w grounded question answering in images"):
  with members:
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "exploring models and data for image question answering"
    - "vqa visual question answering"
    - "visual madlibs fill in the blank image generation and question answering"
    - "learning to answer questions from image using convolutional neural network"
    - "flickr30k entities collecting region to phrase correspondences for richer image to sentence models"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "ask your neurons a neural based approach to answering questions about images"

Connected through hyperedge HE-8 (induced by source paper "movieqa understanding stories in movies through question answering"):
  with members:
    - "exploring models and data for image question answering"
    - "end to end memory networks"
    - "vqa visual question answering"
    - "skip thought vectors"
    - "visual madlibs fill in the blank image generation and question answering"
    - "a dataset for movie description"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "ask your neurons a neural based approach to answering questions about images"

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)