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 data driven approach for compound figure separation using convolutional neural networks"
  Abstract: A key problem in automatic analysis and understanding of scientific papers is to extract semantic information from non-textual paper components like figures, diagrams, tables, etc. Much of this work requires a very first preprocessing step: decomposing compound multi-part figures into individual subfigures. Previous work in compound figure separation has been based on manually designed features and separation rules, which often fail for less common figure types and layouts. Moreover, few implementations for compound figure decomposition are publicly available. #R##N#This paper proposes a data driven approach to separate compound figures using modern deep Convolutional Neural Networks (CNNs) to train the separator in an end-to-end manner. CNNs eliminate the need for manually designing features and separation rules, but require a large amount of annotated training data. We overcome this challenge using transfer learning as well as automatically synthesizing training exemplars. We evaluate our technique on the ImageCLEF Medical dataset, achieving 85.9% accuracy and outperforming previous techniques. We have released our implementation as an easy-to-use Python library, aiming to promote further research in scientific figure mining.

Connected through hyperedge HE-1 (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-2 (induced by source paper "semantic segmentation for compound figures"):
  with members:
    - "yolov3 an incremental improvement"
    - "detecting and recognizing human object interactions"
    - "automatic separation of compound figures in scientific articles"
    - "yolo9000 better faster stronger"
    - "viziometrics analyzing visual information in the scientific literature"
    - "visual relationship detection with language priors"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "extracting scientific figures with distantly supervised neural networks"):
  with members:
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "overfeat integrated recognition localization and detection using convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "figureqa an annotated figure dataset for visual reasoning"):
  with members:
    - "imagination improves multimodal translation"
    - "scatteract automated extraction of data from scatter plots"
    - "a simple neural network module for relational reasoning"
    - "learning to reason end to end module networks for visual question answering"
    - "learning visual reasoning without strong priors"
    - "the something something video database for learning and evaluating visual common sense"
    - "image captioning with semantic attention"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"

Connected through hyperedge HE-5 (induced by source paper "scatteract automated extraction of data from scatter plots"):
  with members:
    - "end to end people detection in crowded scenes"
    - "going deeper with convolutions"
    - "overfeat integrated recognition localization and detection using convolutional networks"

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)