human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "toward automatic comparison of visualization techniques application to graph visualization"):
  Abstract: Many end-user evaluations of data visualization techniques have been run during the last decades. Their results are cornerstones to build efficient visualization systems. However, designing an evaluation is always complex and time-consuming and may end in a lack of statistical evidence. The raising of modern efficient computer vision techniques may help visualization researchers to adjust their evaluation hypothesis and thus reduces the risk of failure. In this paper, we present a methodology that uses such computer vision techniques to automatically compare the efficiency of several visualization techniques. The basis of our methodology is to generate a set of images for each compared visualization technique from a common dataset and to train machine learning models (one for each set and visualization technique) to solve a given task. Our assumption is that the performance of each model allows to compare the efficiencies of the corresponding visualization techniques; as current machine learning models are not capable enough to reflect human capabilities, including their imperfections, such results should be interpreted with caution. However, we argue that using machine learning-based evaluation as a pre-process of standard user evaluations should help researchers to perform a more exhaustive study of the design space and thus should improve the final user evaluation by providing better test cases. To show that our methodology can reproduce, up to a certain level, results of user evaluations, we applied it to compare two mainstream graph visualization techniques: node-link (NL) and adjacency-matrix (MD) diagrams. We partially reproduced a user evaluation from Ghoniem et al. using two well-known deep convolutional neural networks as machine learning-based systems. Our results show up that Ghoniem et al. results can be reproduced automatically at a larger scale with our system.

Includes members:
  Member "evaluating the readability of force directed graph layouts a deep learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a deep generative model for graph layout")
  Member "bigdl a distributed deep learning framework for big data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cluster pruning an efficient filter pruning method for edge ai vision applications")
  Member "can the early human visual system compete with deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "angular visual hardness")
  Member "a study and comparison of human and deep learning recognition performance under visual distortions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "affinity and diversity quantifying mechanisms of data augmentation")
  Member "xception deep learning with depthwise separable convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "25 years of cnns can we compare to human abstraction capabilities":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "evaluating the progress of deep learning for visual relational concepts")
  Member "learning to count with deep object features":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "weakly supervised object detection with 2d and 3d regression neural networks")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

Question: Please tell me which class the source paper 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.AI(Artificial Intelligence)