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 interpretable visual summaries"
  Abstract: A consistent body of research investigates the recurrent visual patterns exploited by deep networks for object classification with the help of diverse visualization techniques. Unfortunately, no effort has been spent in showing that these techniques are effective in leading researchers to univocal and exhaustive explanations. This paper goes in this direction, presenting a visualization framework owing to a group of clusters or summaries, each one formed by crisp image regions focusing on a particular part that the network has exploited with high regularity to classify a given class. In most of the cases, these parts carry a semantic meaning, making the explanation simple and universal. For example, the method suggests that AlexNet, when classifying the ImageNet class "robin", is very sensible to the patterns of the head, the body, the legs, the wings and the tail, providing five summaries where these parts are consistently highlighted. The approach is composed by a sparse optimization step providing sharp image masks whose perturbation causes high loss in the classification. Regions composing the masks are then clustered together by means of a proposal flow-based similarity score, that associates visually similar patterns of diverse objects which are in corresponding positions. The final clusters are visual summaries easy to be interpreted, as found by the very first user study of this kind. The summaries can be also used to compare different architectures: for example, the superiority of GoogleNet w.r.t. AlexNet is explained by our approach since the former gives rise to more summaries, indicating its ability in capturing a higher number of diverse semantic parts.

Connected through hyperedge HE-1 (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"
    - "understanding deep architectures by visual summaries"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"

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)