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: "automatic concept discovery from parallel text and visual corpora"
  Abstract: Humans connect language and vision to perceive the world. How to build a similar connection for computers? One possible way is via visual concepts, which are text terms that relate to visually discriminative entities. We propose an automatic visual concept discovery algorithm using parallel text and visual corpora; it filters text terms based on the visual discriminative power of the associated images, and groups them into concepts using visual and semantic similarities. We illustrate the applications of the discovered concepts using bidirectional image and sentence retrieval task and image tagging task, and show that the discovered concepts not only outperform several large sets of manually selected concepts significantly, but also achieves the state-of-the-art performance in the retrieval task.

Connected through hyperedge HE-1 (induced by source paper "image captioning with unseen objects"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "neural baby talk"
    - "zero shot object detection by hybrid region embedding"
    - "polarity loss for zero shot object detection"
    - "transductive unbiased embedding for zero shot learning"
    - "decoupled novel object captioner"
    - "semantic autoencoder for zero shot learning"

Connected through hyperedge HE-2 (induced by source paper "aesthetic image captioning from weakly labelled photographs"):
  with members:
    - "colornet estimating colorfulness in natural images"
    - "attributes as operators factorizing unseen attribute object compositions"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "a geometry sensitive approach for photographic style classification"
    - "learning to evaluate image captioning"
    - "neural algebra of classifiers"
    - "convolutional image captioning"
    - "a lamp adaptive layout aware multi patch deep convolutional neural network for photo aesthetic assessment"

Connected through hyperedge HE-3 (induced by source paper "cross modal interaction networks for query based moment retrieval in videos"):
  with members:
    - "video re localization"
    - "multilevel language and vision integration for text to clip retrieval"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "exploring visual relationship for image captioning"
    - "attention is all you need"
    - "temporal action detection with structured segment networks"
    - "dense captioning events in videos"
    - "bottom up and top down attention for image captioning and visual question answering"

Connected through hyperedge HE-4 (induced by source paper "mac mining activity concepts for language based temporal localization"):
  with members:
    - "knowledge aided consistency for weakly supervised phrase grounding"
    - "ctap complementary temporal action proposal generation"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "query guided regression network with context policy for phrase grounding"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "temporal action detection with structured segment networks"
    - "cascaded boundary regression for temporal action detection"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"

Connected through hyperedge HE-5 (induced by source paper "vqs linking segmentations to questions and answers for supervised attention in vqa and question focused semantic segmentation"):
  with members:
    - "hadamard product for low rank bilinear pooling"
    - "semantic compositional networks for visual captioning"
    - "segmentation from natural language expressions"
    - "spice semantic propositional image caption evaluation"
    - "multimodal residual learning for visual qa"
    - "grounding of textual phrases in images by reconstruction"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "hierarchical question image co attention for visual question answering"

Connected through hyperedge HE-6 (induced by source paper "mining deep and or object structures via cost sensitive question answer based active annotations"):
  with members:
    - "interpreting cnns via decision trees"
    - "visual interpretability for deep learning a survey"
    - "unsupervised learning of neural networks to explain neural networks"
    - "yolov3 an incremental improvement"
    - "network transplanting"
    - "interpreting cnn knowledge via an explanatory graph"
    - "mining object parts from cnns via active question answering"
    - "interpretable convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "learning without prejudice avoiding bias in webly supervised action recognition"):
  with members:
    - "the thumos challenge on action recognition for videos in the wild"
    - "temporal segment networks towards good practices for deep action recognition"
    - "sympathy for the details dense trajectories and hybrid classification architectures for action recognition"
    - "yfcc100m the new data in multimedia research"
    - "socializing the semantic gap a comparative survey on image tag assignment refinement and retrieval"
    - "webly supervised learning of convolutional networks"
    - "a data driven approach for tag refinement and localization in web videos"
    - "unsupervised learning of video representations using lstms"

Connected through hyperedge HE-8 (induced by source paper "tall temporal activity localization via language query"):
  with members:
    - "turn tap temporal unit regression network for temporal action proposals"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "acd action concept discovery from image sentence corpora"
    - "generation and comprehension of unambiguous object descriptions"
    - "end to end learning of action detection from frame glimpses in videos"
    - "you only look once unified real time object detection"
    - "skip thought vectors"

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)