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: "an interpretable deep hierarchical semantic convolutional neural network for lung nodule malignancy classification"
  Abstract: While deep learning methods are increasingly being applied to tasks such as computer-aided diagnosis, these models are difficult to interpret, do not incorporate prior domain knowledge, and are often considered as a "black-box." The lack of model interpretability hinders them from being fully understood by target users such as radiologists. In this paper, we present a novel interpretable deep hierarchical semantic convolutional neural network (HSCNN) to predict whether a given pulmonary nodule observed on a computed tomography (CT) scan is malignant. Our network provides two levels of output: 1) low-level radiologist semantic features, and 2) a high-level malignancy prediction score. The low-level semantic outputs quantify the diagnostic features used by radiologists and serve to explain how the model interprets the images in an expert-driven manner. The information from these low-level tasks, along with the representations learned by the convolutional layers, are then combined and used to infer the high-level task of predicting nodule malignancy. This unified architecture is trained by optimizing a global loss function including both low- and high-level tasks, thereby learning all the parameters within a joint framework. Our experimental results using the Lung Image Database Consortium (LIDC) show that the proposed method not only produces interpretable lung cancer predictions but also achieves significantly better results compared to common 3D CNN approaches.

Connected through hyperedge HE-1 (induced by source paper "self explaining ai as an alternative to interpretable ai"):
  with members:
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "two models of double descent for weak features"
    - "one explanation does not fit all a toolkit and taxonomy of ai explainability techniques"
    - "deep double descent where bigger models and more data hurt"
    - "identifying weights and architectures of unknown relu networks"
    - "on the in fidelity and sensitivity for explanations"
    - "knowing what you know in brain segmentation using bayesian deep neural networks"
    - "sanity checks for saliency maps"

Connected through hyperedge HE-2 (induced by source paper "gated dilated networks for lung nodule classification in ct scans"):
  with members:
    - "lung nodule classification using deep local global networks"
    - "risk stratification of lung nodules using 3d cnn based multi task learning"
    - "dilated residual networks"
    - "rethinking atrous convolution for semantic image segmentation"
    - "aggregated residual transformations for deep neural networks"
    - "identity mappings in deep residual networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "neural machine translation in linear time"

Connected through hyperedge HE-3 (induced by source paper "a feature transfer enabled multi task deep learning model on medical imaging"):
  with members:
    - "sd cnn a shallow deep cnn for improved breast cancer diagnosis"
    - "a deep learning model integrating fcnns and crfs for brain tumor segmentation"
    - "brain tumor segmentation with deep neural networks"
    - "a survey on deep learning in medical image analysis"
    - "an overview of multi task learning in deep neural networks"
    - "mask r cnn"
    - "the importance of skip connections in biomedical image segmentation"
    - "improving computer aided detection using convolutional neural networks and random view aggregation"

Connected through hyperedge HE-4 (induced by source paper "sisc end to end interpretable discovery radiomics driven lung cancer prediction via stacked interpretable sequencing cells"):
  with members:
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "diagnostic classification of lung nodules using 3d neural networks"
    - "explaining the unexplained a class enhanced attentive response clear approach to understanding deep neural networks"
    - "opening the black box of financial ai with clear trade a class enhanced attentive response approach for explaining and visualizing deep learning driven stock market prediction"
    - "deeplung 3d deep convolutional nets for automated pulmonary nodule detection and classification"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "characterization of lung nodule malignancy using hybrid shape and appearance features"
    - "understanding neural networks through deep visualization"

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)