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: "viziometrics analyzing visual information in the scientific literature"
  Abstract: Scientific results are communicated visually in the literature through diagrams, visualizations, and photographs. These information-dense objects have been largely ignored in bibliometrics and scientometrics studies when compared to citations and text. In this paper, we use techniques from computer vision and machine learning to classify more than 8 million figures from PubMed into 5 figure types and study the resulting patterns of visual information as they relate to impact. We find that the distribution of figures and figure types in the literature has remained relatively constant over time, but can vary widely across field and topic. Remarkably, we find a significant correlation between scientific impact and the use of visual information, where higher impact papers tend to include more diagrams, and to a lesser extent more plots and photographs. To explore these results and other ways of extracting this visual information, we have built a visual browser to illustrate the concept and explore design alternatives for supporting viziometric analysis and organizing visual information. We use these results to articulate a new research agenda -- viziometrics -- to study the organization and presentation of visual information in the scientific literature.

Connected through hyperedge HE-1 (induced by source paper "semantic segmentation for compound figures"):
  with members:
    - "yolov3 an incremental improvement"
    - "detecting and recognizing human object interactions"
    - "a data driven approach for compound figure separation using convolutional neural networks"
    - "automatic separation of compound figures in scientific articles"
    - "yolo9000 better faster stronger"
    - "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-2 (induced by source paper "viznet towards a large scale visualization learning and benchmarking repository"):
  with members:
    - "idebench a benchmark for interactive data exploration"
    - "beyond heuristics learning visualization design"
    - "vizml a machine learning approach to visualization recommendation"
    - "data2vis automatic generation of data visualizations using sequence to sequence recurrent neural networks"
    - "task based effectiveness of basic visualizations"
    - "beagle automated extraction and interpretation of visualizations from the web"
    - "squad 100 000 questions for machine comprehension of text"
    - "boomerang rebounding the consequences of reputation feedback on crowdsourcing platforms"

Connected through hyperedge HE-3 (induced by source paper "analyzing linguistic complexity and scientific impact"):
  with members:
    - "examining scientific writing styles from the perspective of linguistic complexity"
    - "a review of the literature on citation impact indicators"
    - "caveats for using statistical significance tests in research assessments"
    - "quantifying long term scientific impact"
    - "tnt a statistical part of speech tagger"

Connected through hyperedge HE-4 (induced by source paper "a data driven approach for compound figure separation using convolutional neural networks"):
  with members:
    - "automatic separation of compound figures in scientific articles"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "you only look once unified real time object detection"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal 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.SI(Social and Information Networks)