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: "unsupervised visual sense disambiguation for verbs using multimodal embeddings"
  Abstract: We introduce a new task, visual sense disambiguation for verbs: given an image and a verb, assign the correct sense of the verb, i.e., the one that describes the action depicted in the image. Just as textual word sense disambiguation is useful for a wide range of NLP tasks, visual sense disambiguation can be useful for multimodal tasks such as image retrieval, image description, and text illustration. We introduce VerSe, a new dataset that augments existing multimodal datasets (COCO and TUHOI) with sense labels. We propose an unsupervised algorithm based on Lesk which performs visual sense disambiguation using textual, visual, or multimodal embeddings. We find that textual embeddings perform well when gold-standard textual annotations (object labels and image descriptions) are available, while multimodal embeddings perform well on unannotated images. We also verify our findings by using the textual and multimodal embeddings as features in a supervised setting and analyse the performance of visual sense disambiguation task. VerSe is made publicly available and can be downloaded at: this https URL

Connected through hyperedge HE-1 (induced by source paper "learning visual actions using multiple verb only labels"):
  with members:
    - "action2vec a crossmodal embedding approach to action learning"
    - "scaling egocentric vision the epic kitchens dataset"
    - "end to end dense video captioning with masked transformer"
    - "reconstruction network for video captioning"
    - "what actions are needed for understanding human actions in videos"
    - "video captioning via hierarchical reinforcement learning"
    - "convolutional image captioning"
    - "zero shot activity recognition with verb attribute induction"

Connected through hyperedge HE-2 (induced by source paper "cross lingual visual verb sense disambiguation"):
  with members:
    - "lessons learned in multilingual grounded language learning"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "sockeye a toolkit for neural machine translation"
    - "cuni system for the wmt17 multimodal translation task"
    - "findings of the second shared task on multimodal machine translation and multilingual image description"
    - "image pivoting for learning multilingual multimodal representations"
    - "lium cvc submissions for wmt17 multimodal translation task"

Connected through hyperedge HE-3 (induced by source paper "dynamically visual disambiguation of keyword based image search"):
  with members:
    - "deep representation learning for road detection through siamese network"
    - "hierarchical long short term concurrent memory for human interaction recognition"
    - "towards automatic construction of diverse high quality image dataset"
    - "exploiting web images for dataset construction a domain robust approach"
    - "frankenstein learning deep face representations using small data"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "learning deep features for discriminative localization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "towards an unequivocal representation of actions"):
  with members:
    - "what actions are needed for understanding human actions in videos"
    - "improving classification by improving labelling introducing probabilistic multi label object interaction recognition"
    - "the kinetics human action video dataset"
    - "sembed semantic embedding of egocentric action videos"
    - "hcp a flexible cnn framework for multi label image classification"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "convolutional two stream network fusion for video action recognition"
    - "describing common human visual actions in images"

Connected through hyperedge HE-5 (induced by source paper "a survey of machine learning for big code and naturalness"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "nl2bash a corpus and semantic parser for natural language interface to the linux operating system"
    - "deep reinforcement learning for programming language correction"
    - "recent advances in neural program synthesis"
    - "robustfill neural program learning under noisy i o"
    - "codesum translate program language to natural language"
    - "autofolding for source code summarization"
    - "neural attribute machines for program generation"

Connected through hyperedge HE-6 (induced by source paper "findings of the second shared task on multimodal machine translation and multilingual image description"):
  with members:
    - "cross linguistic differences and similarities in image descriptions"
    - "imagination improves multimodal translation"
    - "nmtpy a flexible toolkit for advanced neural machine translation systems"
    - "osu multimodal machine translation system report"
    - "stair captions constructing a large scale japanese image caption dataset"
    - "multimodal pivots for image caption translation"
    - "multi30k multilingual english german image descriptions"
    - "edinburgh neural machine translation systems for wmt 16"

Connected through hyperedge HE-7 (induced by source paper "an analysis of action recognition datasets for language and vision tasks"):
  with members:
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "visual relationship detection with language priors"
    - "learning models for actions and person object interactions with transfer to question answering"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "resolving language and vision ambiguities together joint segmentation prepositional attachment resolution in captioned scenes"):
  with members:
    - "do you see what i mean visual resolution of linguistic ambiguities"
    - "vqa visual question answering"
    - "visual madlibs fill in the blank image generation and question answering"
    - "ask your neurons a neural based approach to answering questions about images"
    - "cider consensus based image description evaluation"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "submodular meets structured finding diverse subsets in exponentially large structured item sets"
    - "from captions to visual concepts and back"

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.CL(Computation and Language)