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: "video in sentences out"
  Abstract: We present a system that produces sentential descriptions of video: who did what to whom, and where and how they did it. Action class is rendered as a verb, participant objects as noun phrases, properties of those objects as adjectival modifiers in those noun phrases, spatial relations between those participants as prepositional phrases, and characteristics of the event as prepositional-phrase adjuncts and adverbial modifiers. Extracting the information needed to render these linguistic entities requires an approach to event recognition that recovers object tracks, the trackto-role assignments, and changing body posture.

Connected through hyperedge HE-1 (induced by source paper "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"):
  with members:
    - "video description a survey of methods datasets and evaluation metrics"
    - "less is more picking informative frames for video captioning"
    - "reconstruction network for video captioning"
    - "memory augmented attention modelling for videos"
    - "hierarchical boundary aware neural encoder for video captioning"
    - "video captioning with transferred semantic attributes"
    - "recurrent memory addressing for describing videos"
    - "semantic compositional networks for visual captioning"

Connected through hyperedge HE-2 (induced by source paper "captioning near future activity sequences"):
  with members:
    - "move forward and tell a progressive generator of video descriptions"
    - "when will you do what anticipating temporal occurrences of activities"
    - "dense captioning events in videos"
    - "estimating human intent for physical human robot co manipulation"
    - "towards automatic learning of procedures from web instructional videos"
    - "recurrent topic transition gan for visual paragraph generation"
    - "spice semantic propositional image caption evaluation"
    - "first person activity forecasting with online inverse reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "knowledge rich image gist understanding beyond literal meaning"):
  with members:
    - "exploring representativeness and informativeness for active learning"
    - "rich image captioning in the wild"
    - "exploring models and data for image question answering"
    - "imagenet large scale visual recognition challenge"
    - "from captions to visual concepts and back"
    - "microsoft coco common objects in context"
    - "tagme on the fly annotation of short text fragments by wikipedia entities"

Connected through hyperedge HE-4 (induced by source paper "query adaptive video summarization via quality aware relevance estimation"):
  with members:
    - "to click or not to click automatic selection of beautiful thumbnails from videos"
    - "video summarization with long short term memory"
    - "query focused extractive video summarization"
    - "video2gif automatic generation of animated gifs from video"
    - "a data driven approach for tag refinement and localization in web videos"
    - "tagbook a semantic video representation without supervision for event detection"
    - "objects2action classifying and localizing actions without any video example"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "title generation for user generated videos"):
  with members:
    - "jointly modeling embedding and translation to bridge video and language"
    - "generation and comprehension of unambiguous object descriptions"
    - "the long short story of movie description"
    - "a dataset for movie description"
    - "densecap fully convolutional localization networks for dense captioning"
    - "hierarchical recurrent neural encoder for video representation with application to captioning"
    - "using descriptive video services to create a large data source for video annotation research"
    - "learning like a child fast novel visual concept learning from sentence descriptions of images"

Connected through hyperedge HE-6 (induced by source paper "bayesian inference of recursive sequences of group activities from tracks"):
  with members:
    - "group event detection with a varying number of group members for video surveillance"
    - "joint inference of groups events and human roles in aerial videos"
    - "a generic framework for video understanding applied to group behavior recognition"

Connected through hyperedge HE-7 (induced by source paper "video paragraph captioning using hierarchical recurrent neural networks"):
  with members:
    - "jointly modeling embedding and translation to bridge video and language"
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "learning like a child fast novel visual concept learning from sentence descriptions of images"
    - "sequence level training with recurrent neural networks"
    - "sequence to sequence video to text"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "microsoft coco captions data collection and evaluation server"
    - "semantically conditioned lstm based natural language generation for spoken dialogue systems"

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)