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: "joint video and text parsing for understanding events and answering queries"
  Abstract: We propose a framework for parsing video and text jointly for understanding events and answering user queries. Our framework produces a parse graph that represents the compositional structures of spatial information (objects and scenes), temporal information (actions and events) and causal information (causalities between events and fluents) in the video and text. The knowledge representation of our framework is based on a spatial-temporal-causal And-Or graph (S/T/C-AOG), which jointly models possible hierarchical compositions of objects, scenes and events as well as their interactions and mutual contexts, and specifies the prior probabilistic distribution of the parse graphs. We present a probabilistic generative model for joint parsing that captures the relations between the input video/text, their corresponding parse graphs and the joint parse graph. Based on the probabilistic model, we propose a joint parsing system consisting of three modules: video parsing, text parsing and joint inference. Video parsing and text parsing produce two parse graphs from the input video and text respectively. The joint inference module produces a joint parse graph by performing matching, deduction and revision on the video and text parse graphs. The proposed framework has the following objectives: Firstly, we aim at deep semantic parsing of video and text that goes beyond the traditional bag-of-words approaches; Secondly, we perform parsing and reasoning across the spatial, temporal and causal dimensions based on the joint S/T/C-AOG representation; Thirdly, we show that deep joint parsing facilitates subsequent applications such as generating narrative text descriptions and answering queries in the forms of who, what, when, where and why. We empirically evaluated our system based on comparison against ground-truth as well as accuracy of query answering and obtained satisfactory results.

Connected through hyperedge HE-1 (induced by source paper "component analysis for visual question answering architectures"):
  with members:
    - "m 2 meshed memory transformer for image captioning"
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "language features matter effective language representations for vision language tasks"
    - "evalai towards better evaluation systems for ai agents"
    - "murel multimodal relational reasoning for visual question answering"
    - "deep learning using rectified linear units relu"
    - "stacked cross attention for image text matching"
    - "generalized hadamard product fusion operators for visual question answering"

Connected through hyperedge HE-2 (induced by source paper "ok vqa a visual question answering benchmark requiring external knowledge"):
  with members:
    - "bilinear attention networks"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "straight to the facts learning knowledge base retrieval for factual visual question answering"
    - "out of the box reasoning with graph convolution nets for factual visual question answering"
    - "inferring and executing programs for visual reasoning"
    - "mutan multimodal tucker fusion for visual question answering"
    - "an implementation of faster rcnn with study for region sampling"
    - "incorporating external knowledge to answer open domain visual questions with dynamic memory networks"

Connected through hyperedge HE-3 (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-4 (induced by source paper "vision to language tasks based on attributes and attention mechanism"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "attend to you personalized image captioning with context sequence memory networks"
    - "tgif qa toward spatio temporal reasoning in visual question answering"
    - "hierarchical boundary aware neural encoder for video captioning"
    - "dual attention networks for multimodal reasoning and matching"
    - "image captioning with semantic attention"
    - "hierarchical question image co attention for visual question answering"

Connected through hyperedge HE-5 (induced by source paper "improving visual question answering by referring to generated paragraph captions"):
  with members:
    - "bottom up and top down attention for image captioning and visual question answering"
    - "recurrent topic transition gan for visual paragraph generation"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"
    - "bidirectional attention flow for machine comprehension"
    - "a hierarchical approach for generating descriptive image paragraphs"
    - "hierarchical question image co attention for visual question answering"
    - "temporal segment networks towards good practices for deep action recognition"
    - "self critical sequence training for image captioning"

Connected through hyperedge HE-6 (induced by source paper "out of sight but not out of mind an answer set programming based online abduction framework for visual sensemaking in autonomous driving"):
  with members:
    - "yolov3 an incremental improvement"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "spatial as deep spatial cnn for traffic scene understanding"
    - "mot16 a benchmark for multi object tracking"
    - "simple online and realtime tracking"
    - "ssd single shot multibox detector"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "clingo asp control preliminary report"

Connected through hyperedge HE-7 (induced by source paper "mining interpretable aog representations from convolutional networks via active question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "one pixel attack for fooling deep neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "examining cnn representations with respect to dataset bias"
    - "mining object parts from cnns via active question answering"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "harnessing deep neural networks with logic rules"
    - "growing interpretable part graphs on convnets via multi shot learning"

Connected through hyperedge HE-8 (induced by source paper "a better way to attend attention with trees for video question answering"):
  with members:
    - "tree structured reinforcement learning for sequential object localization"
    - "tree to sequence attentional neural machine translation"
    - "enhancing and combining sequential and tree lstm for natural language inference"
    - "image captioning with semantic attention"
    - "a focused dynamic attention model for visual question answering"
    - "enhanced lstm for natural language inference"
    - "video fill in the blank with merging lstms"
    - "hierarchical question image co attention for visual question answering"

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)