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: "figureqa an annotated figure dataset for visual reasoning"
  Abstract: We introduce FigureQA, a visual reasoning corpus of over one million question-answer pairs grounded in over 100,000 images. The images are synthetic, scientific-style figures from five classes: line plots, dot-line plots, vertical and horizontal bar graphs, and pie charts. We formulate our reasoning task by generating questions from 15 templates; questions concern various relationships between plot elements and examine characteristics like the maximum, the minimum, area-under-the-curve, smoothness, and intersection. To resolve, such questions often require reference to multiple plot elements and synthesis of information distributed spatially throughout a figure. To facilitate the training of machine learning systems, the corpus also includes side data that can be used to formulate auxiliary objectives. In particular, we provide the numerical data used to generate each figure as well as bounding-box annotations for all plot elements. We study the proposed visual reasoning task by training several models, including the recently proposed Relation Network as a strong baseline. Preliminary results indicate that the task poses a significant machine learning challenge. We envision FigureQA as a first step towards developing models that can intuitively recognize patterns from visual representations of data.

Connected through hyperedge HE-1 (induced by source paper "plotqa reasoning over scientific plots"):
  with members:
    - "towards vqa models that can read"
    - "bilinear attention networks"
    - "dvqa understanding data visualizations via question answering"
    - "scatteract automated extraction of data from scatter plots"
    - "neural multi step reasoning for question answering on semi structured tables"
    - "mask r cnn"
    - "hadamard product for low rank bilinear pooling"
    - "training recurrent answering units with joint loss minimization for vqa"

Connected through hyperedge HE-2 (induced by source paper "scene text visual question answering"):
  with members:
    - "towards vqa models that can read"
    - "icdar 2019 competition on scene text visual question answering"
    - "single shot scene text retrieval"
    - "fots fast oriented text spotting with a unified network"
    - "an end to end textspotter with explicit alignment and attention"
    - "tallyqa answering complex counting questions"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "textboxes a single shot oriented scene text detector"

Connected through hyperedge HE-3 (induced by source paper "towards vqa models that can read"):
  with members:
    - "bilinear attention networks"
    - "learning to count objects in natural images for visual question answering"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "disentangling language and knowledge in task oriented dialogs"
    - "rosetta large scale system for text detection and recognition in images"
    - "dvqa understanding data visualizations via question answering"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-4 (induced by source paper "an empirical study of batch normalization and group normalization in conditional computation"):
  with members:
    - "classification accuracy score for conditional generative models"
    - "batch normalization is a cause of adversarial vulnerability"
    - "fixup initialization residual learning without normalization"
    - "group normalization"
    - "systematic generalization what is required and can it be learned"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-5 (induced by source paper "procedural reasoning networks for understanding multimodal procedures"):
  with members:
    - "be consistent improving procedural text comprehension using label consistency"
    - "the natural language decathlon multitask learning as question answering"
    - "recipe1m a dataset for learning cross modal embeddings for cooking recipes and food images"
    - "building dynamic knowledge graphs from text using machine reading comprehension"
    - "neural models for reasoning over multiple mentions using coreference"
    - "relational recurrent neural networks"
    - "tracking state changes in procedural text a challenge dataset and models for process paragraph comprehension"
    - "allennlp a deep semantic natural language processing platform"

Connected through hyperedge HE-6 (induced by source paper "answering questions about data visualizations using efficient bimodal fusion"):
  with members:
    - "towards vqa models that can read"
    - "answer them all toward universal visual question answering models"
    - "the visual qa devil in the details the impact of early fusion and batch norm on clevr"
    - "bilinear attention networks"
    - "tallyqa answering complex counting questions"
    - "figurenet a deep learning model for question answering on scientific plots"
    - "dvqa understanding data visualizations via question answering"
    - "scatteract automated extraction of data from scatter plots"

Connected through hyperedge HE-7 (induced by source paper "figure captioning with reasoning and sequence level training"):
  with members:
    - "dvqa understanding data visualizations via question answering"
    - "visual question answering datasets algorithms and future challenges"
    - "a simple neural network module for relational reasoning"
    - "a focused dynamic attention model for visual question answering"
    - "dualnet domain invariant network for visual question answering"
    - "multimodal residual learning for visual qa"
    - "an actor critic algorithm for sequence prediction"
    - "learning to compose neural networks for question answering"

Connected through hyperedge HE-8 (induced by source paper "chartnet visual reasoning over statistical charts using mac networks"):
  with members:
    - "compositional attention networks for machine reasoning"
    - "inferring and executing programs for visual reasoning"
    - "learning to reason end to end module networks for visual question answering"
    - "hadamard product for low rank bilinear pooling"
    - "a focused dynamic attention model for visual question answering"
    - "learning to compose neural networks for question answering"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "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)