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: "a multi scale multiple instance video description network"
  Abstract: Generating natural language descriptions for in-the-wild videos is a challenging task. Most state-of-the-art methods for solving this problem borrow existing deep convolutional neural network (CNN) architectures (AlexNet, GoogLeNet) to extract a visual representation of the input video. However, these deep CNN architectures are designed for single-label centered-positioned object classification. While they generate strong semantic features, they have no inherent structure allowing them to detect multiple objects of different sizes and locations in the frame. Our paper tries to solve this problem by integrating the base CNN into several fully convolutional neural networks (FCNs) to form a multi-scale network that handles multiple receptive field sizes in the original image. FCNs, previously applied to image segmentation, can generate class heat-maps efficiently compared to sliding window mechanisms, and can easily handle multiple scales. To further handle the ambiguity over multiple objects and locations, we incorporate the Multiple Instance Learning mechanism (MIL) to consider objects in different positions and at different scales simultaneously. We integrate our multi-scale multi-instance architecture with a sequence-to-sequence recurrent neural network to generate sentence descriptions based on the visual representation. Ours is the first end-to-end trainable architecture that is capable of multi-scale region processing. Evaluation on a Youtube video dataset shows the advantage of our approach compared to the original single-scale whole frame CNN model. Our flexible and efficient architecture can potentially be extended to support other video processing tasks.

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 "multilevel language and vision integration for text to clip retrieval"):
  with members:
    - "learning unsupervised visual grounding through semantic self supervision"
    - "joint event detection and description in continuous video streams"
    - "modulating early visual processing by language"
    - "objects that sound"
    - "learning two branch neural networks for image text matching tasks"
    - "single shot temporal action detection"
    - "dense captioning events in videos"
    - "r c3d region convolutional 3d network for temporal activity detection"

Connected through hyperedge HE-4 (induced by source paper "joint event detection and description in continuous video streams"):
  with members:
    - "dense captioning events in videos"
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "tall temporal activity localization via language query"
    - "turn tap temporal unit regression network for temporal action proposals"
    - "localizing moments in video with natural language"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "temporal activity detection in untrimmed videos with recurrent neural networks"
    - "a hierarchical approach for generating descriptive image paragraphs"

Connected through hyperedge HE-5 (induced by source paper "video captioning with boundary aware hierarchical language decoding and joint video prediction"):
  with members:
    - "hierarchical lstm with adjusted temporal attention for video captioning"
    - "from deterministic to generative multi modal stochastic rnns for video captioning"
    - "hierarchical boundary aware neural encoder for video captioning"
    - "multi task video captioning with video and entailment generation"
    - "video captioning with transferred semantic attributes"
    - "semantic compositional networks for visual captioning"
    - "jointly modeling embedding and translation to bridge video and language"
    - "the long short story of movie description"

Connected through hyperedge HE-6 (induced by source paper "weakly supervised dense event captioning in videos"):
  with members:
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "end to end dense video captioning with masked transformer"
    - "jointly localizing and describing events for dense video captioning"
    - "to find where you talk temporal sentence localization in video with attention based location regression"
    - "the kinetics human action video dataset"
    - "turn tap temporal unit regression network for temporal action proposals"
    - "boosting image captioning with attributes"
    - "sequence to sequence video to text"

Connected through hyperedge HE-7 (induced by source paper "deep learning for video classification and captioning"):
  with members:
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "the thumos challenge on action recognition for videos in the wild"
    - "real time action recognition with enhanced motion vector cnns"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "temporal segment networks towards good practices for deep action recognition"
    - "improving lstm based video description with linguistic knowledge mined from text"
    - "videolstm convolves attends and flows for action recognition"

Connected through hyperedge HE-8 (induced by source paper "dense captioning events in videos"):
  with members:
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "a hierarchical approach for generating descriptive image paragraphs"
    - "learning joint representations of videos and sentences with web image search"
    - "embracing error to enable rapid crowdsourcing"
    - "dense captioning with joint inference and visual context"
    - "jointly modeling embedding and translation to bridge video and language"
    - "the long short story of movie description"

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)