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: "understanding deep architectures by visual summaries"
  Abstract: In deep learning, visualization techniques extract the salient patterns exploited by deep networks for image classification, focusing on single images; no effort has been spent in investigating whether these patterns are systematically related to precise semantic entities over multiple images belonging to a same class, thus failing to capture the very understanding of the image class the network has realized. This paper goes in this direction, presenting a visualization framework which produces a group of clusters or summaries, each one formed by crisp salient image regions focusing on a particular part that the network has exploited with high regularity to decide for a given class. The approach is based on a sparse optimization step providing sharp image saliency masks that are clustered together by means of a semantic flow similarity measure. The summaries communicate clearly what a network has exploited of a particular image class, and this is proved through automatic image tagging and with a user study. Beyond the deep network understanding, summaries are also useful for many quantitative reasons: their number is correlated with ability of a network to classify (more summaries, better performances), and they can be used to improve the classification accuracy of a network through summary-driven specializations.

Connected through hyperedge HE-1 (induced by source paper "sam the sensitivity of attribution methods to hyperparameters"):
  with members:
    - "interpretable and fine grained visual explanations for convolutional neural networks"
    - "towards explainable artificial intelligence"
    - "visualizing deep networks by optimizing with integrated gradients"
    - "removing input features via a generative model to explain their attributions to classifier s decisions"
    - "methods for interpreting and understanding deep neural networks"
    - "interpreting and explaining deep neural networks for classification of audio signals"
    - "explaining image classifiers by counterfactual generation"
    - "rise randomized input sampling for explanation of black box models"

Connected through hyperedge HE-2 (induced by source paper "a tour of convolutional networks guided by linear interpreters"):
  with members:
    - "towards explanation of dnn based prediction with guided feature inversion"
    - "visual interpretability for deep learning a survey"
    - "prediction error meta classification in semantic segmentation detection via aggregated dispersion measures of softmax probabilities"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "the matrix calculus you need for deep learning"

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)