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 diagram is worth a dozen images"
  Abstract: Diagrams are common tools for representing complex concepts, relationships and events, often when it would be difficult to portray the same information with natural images. Understanding natural images has been extensively studied in computer vision, while diagram understanding has received little attention. In this paper, we study the problem of diagram interpretation and reasoning, the challenging task of identifying the structure of a diagram and the semantics of its constituents and their relationships. We introduce Diagram Parse Graphs (DPG) as our representation to model the structure of diagrams. We define syntactic parsing of diagrams as learning to infer DPGs for diagrams and study semantic interpretation and reasoning of diagrams in the context of diagram question answering. We devise an LSTM-based method for syntactic parsing of diagrams and introduce a DPG-based attention model for diagram question answering. We compile a new dataset of diagrams with exhaustive annotations of constituents and relationships for over 5,000 diagrams and 15,000 questions and answers. Our results show the significance of our models for syntactic parsing and question answering in diagrams using DPGs.

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"
    - "figureqa an annotated figure dataset for visual reasoning"
    - "mask r cnn"
    - "hadamard product for low rank bilinear pooling"

Connected through hyperedge HE-2 (induced by source paper "towards automated infographic design deep learning based auto extraction of extensible timeline"):
  with members:
    - "synthetically trained icon proposals for parsing and summarizing infographics"
    - "vizml a machine learning approach to visualization recommendation"
    - "data2vis automatic generation of data visualizations using sequence to sequence recurrent neural networks"
    - "dvqa understanding data visualizations via question answering"
    - "focal loss for dense object detection"
    - "dynamic routing between capsules"
    - "learning visual importance for graphic designs and data visualizations"
    - "understanding infographics through textual and visual tag prediction"

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"
    - "figureqa an annotated figure dataset for visual reasoning"

Connected through hyperedge HE-4 (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-5 (induced by source paper "look read and enrich learning from scientific figures and their captions"):
  with members:
    - "scibert pretrained contextualized embeddings for scientific text"
    - "finding beans in burgers deep semantic visual embedding with localization"
    - "deep contextualized word representations"
    - "construction of the literature graph in semantic scholar"
    - "learning two branch neural networks for image text matching tasks"
    - "look listen and learn"
    - "vse improving visual semantic embeddings with hard negatives"
    - "bidirectional attention flow for machine comprehension"

Connected through hyperedge HE-6 (induced by source paper "data interpretation over plots"):
  with members:
    - "dvqa understanding data visualizations via question answering"
    - "scatteract automated extraction of data from scatter plots"
    - "a simple neural network module for relational reasoning"
    - "neural multi step reasoning for question answering on semi structured tables"
    - "mask r cnn"
    - "training recurrent answering units with joint loss minimization for vqa"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-7 (induced by source paper "classifying diagrams and their parts using graph neural networks a comparison of crowd sourced and expert annotations"):
  with members:
    - "understanding categorizing and predicting semantic image text relations"
    - "cite a corpus of image text discourse relations"
    - "simplifying graph convolutional networks"
    - "deep graph library towards efficient and scalable deep learning on graphs"
    - "a comprehensive survey on graph neural networks"
    - "pitfalls of graph neural network evaluation"
    - "tune a research platform for distributed model selection and training"
    - "dynamic graph generation network generating relational knowledge from diagrams"

Connected through hyperedge HE-8 (induced by source paper "delineating knowledge domains in the scientific literature using visual information"):
  with members:
    - "vitor learning to rank webpages based on visual features"
    - "multidec multi modal clustering of image caption pairs"
    - "a century of science globalization of scientific collaborations citations and innovations"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "distributed representations of words and phrases and their compositionality"
    - "creation and use of citations in the ads"

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)