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: "describing common human visual actions in images"
  Abstract: Which common human actions and interactions are recognizable in monocular still images? Which involve objects and/or other people? How many is a person performing at a time? We address these questions by exploring the actions and interactions that are detectable in the images of the MS COCO dataset. We make two main contributions. First, a list of 140 common `visual actions', obtained by analyzing the largest on-line verb lexicon currently available for English (VerbNet) and human sentences used to describe images in MS COCO. Second, a complete set of annotations for those `visual actions', composed of subject-object and associated verb, which we call COCO-a (a for `actions'). COCO-a is larger than existing action datasets in terms of number of actions and instances of these actions, and is unique because it is data-driven, rather than experimenter-biased. Other unique features are that it is exhaustive, and that all subjects and objects are localized. A statistical analysis of the accuracy of our annotations and of each action, interaction and subject-object combination is provided.

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 "visual understanding and narration a deeper understanding and explanation of visual scenes"):
  with members:
    - "from recognition to cognition visual commonsense reasoning"
    - "a pipeline for creative visual storytelling"
    - "temporal event knowledge acquisition via identifying narratives"
    - "scoutbot a dialogue system for collaborative navigation"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "dock detecting objects by transferring common sense knowledge"
    - "hierarchical neural story generation"
    - "hierarchically attentive rnn for album summarization and storytelling"

Connected through hyperedge HE-3 (induced by source paper "detecting unseen visual relations using analogies"):
  with members:
    - "ican instance centric attention network for human object interaction detection"
    - "large scale visual relationship understanding"
    - "learning human object interactions by graph parsing neural networks"
    - "detecting and recognizing human object interactions"
    - "a simple neural network module for relational reasoning"
    - "weakly supervised learning of visual relations"
    - "detecting visual relationships with deep relational networks"
    - "relnet end to end modeling of entities relations"

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"
    - "unsupervised visual sense disambiguation for verbs using multimodal embeddings"
    - "convolutional two stream network fusion for video action recognition"

Connected through hyperedge HE-5 (induced by source paper "situation recognition with graph neural networks"):
  with members:
    - "teaching machines to describe images via natural language feedback"
    - "annotating object instances with a polygon rnn"
    - "visual translation embedding network for visual relation detection"
    - "semantic object parsing with graph lstm"
    - "gated graph sequence neural networks"
    - "places an image database for deep scene understanding"
    - "semi supervised classification with graph convolutional networks"
    - "the more you know using knowledge graphs for image classification"

Connected through hyperedge HE-6 (induced by source paper "attentional pooling for action recognition"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "attention is all you need"
    - "a simple neural network module for relational reasoning"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "the kinetics human action video dataset"
    - "chained multi stream networks exploiting pose motion and appearance for action classification and detection"
    - "hadamard product for low rank bilinear pooling"
    - "hollywood in homes crowdsourcing data collection for activity understanding"

Connected through hyperedge HE-7 (induced by source paper "recurrent models for situation recognition"):
  with members:
    - "structured attention networks"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "image captioning with semantic attention"
    - "spice semantic propositional image caption evaluation"
    - "boosting image captioning with attributes"
    - "commonly uncommon semantic sparsity in situation recognition"
    - "learning models for actions and person object interactions with transfer to question answering"
    - "language models for image captioning the quirks and what works"

Connected through hyperedge HE-8 (induced by source paper "detecting and recognizing human object interactions"):
  with members:
    - "learning to detect human object interactions"
    - "mask r cnn"
    - "convolutional pose machines"
    - "feature pyramid networks for object detection"
    - "visual relationship detection with language priors"
    - "learning models for actions and person object interactions with transfer to question answering"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "contextual action recognition with r cnn"

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)