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: "analyzing the noise robustness of deep neural networks"
  Abstract: Deep neural networks (DNNs) are vulnerable to maliciously generated adversarial examples. These examples are intentionally designed by making imperceptible perturbations and often mislead a DNN into making an incorrect prediction. This phenomenon means that there is significant risk in applying DNNs to safety-critical applications, such as driverless cars. To address this issue, we present a visual analytics approach to explain the primary cause of the wrong predictions introduced by adversarial examples. The key is to analyze the datapaths of the adversarial examples and compare them with those of the normal examples. A datapath is a group of critical neurons and their connections. To this end, we formulate the datapath extraction as a subset selection problem and approximately solve it based on back-propagation. A multi-level visualization consisting of a segmented DAG (layer level), an Euler diagram (feature map level), and a heat map (neuron level), has been designed to help experts investigate datapaths from the high-level layers to the detailed neuron activations. Two case studies are conducted that demonstrate the promise of our approach in support of explaining the working mechanism of adversarial examples.

Connected through hyperedge HE-1 (induced by source paper "explainer a visual analytics framework for interactive and explainable machine learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "speculative execution for guided visual analytics"
    - "gan lab understanding complex deep generative models using interactive visual experimentation"
    - "human centered tools for coping with imperfect algorithms during medical decision making"
    - "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"
    - "manifold a model agnostic framework for interpretation and diagnosis of machine learning models"
    - "rulematrix visualizing and understanding classifiers with rules"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"

Connected through hyperedge HE-2 (induced by source paper "defective convolutional layers learn robust cnns"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "dropping pixels for adversarial robustness"
    - "adversarial defense by stratified convolutional sparse coding"
    - "dropblock a regularization method for convolutional networks"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "sok security and privacy in machine learning"
    - "defense against adversarial attacks using high level representation guided denoiser"

Connected through hyperedge HE-3 (induced by source paper "summit scaling deep learning interpretability by visualizing activation and attribution summarizations"):
  with members:
    - "gan lab understanding complex deep generative models using interactive visual experimentation"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "taskonomy disentangling task transfer learning"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "methods for interpreting and understanding deep neural networks"
    - "choose your neuron incorporating domain knowledge through neuron importance"
    - "do convolutional neural networks learn class hierarchy"
    - "explaining explanations an overview of interpretability of machine learning"

Connected through hyperedge HE-4 (induced by source paper "explaining vulnerabilities to adversarial machine learning through visual analytics"):
  with members:
    - "gan lab understanding complex deep generative models using interactive visual experimentation"
    - "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "rulematrix visualizing and understanding classifiers with rules"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "poison frogs targeted clean label poisoning attacks on neural networks"
    - "certified defenses for data poisoning attacks"

Connected through hyperedge HE-5 (induced by source paper "scalable topological data analysis and visualization for evaluating data driven models in scientific applications"):
  with members:
    - "rulematrix visualizing and understanding classifiers with rules"
    - "a unified approach to interpreting model predictions"
    - "why should i trust you explaining the predictions of any classifier"
    - "understanding neural networks through deep visualization"
    - "visualizing and understanding convolutional networks"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-6 (induced by source paper "recent research advances on interactive machine learning"):
  with members:
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "the subspace voyager exploring high dimensional data along a continuum of salient 3d subspaces"
    - "do convolutional neural networks learn class hierarchy"
    - "visual analytics for explainable deep learning"
    - "understanding hidden memories of recurrent neural networks"
    - "towards better analysis of machine learning models a visual analytics perspective"
    - "towards better analysis of deep convolutional neural networks"
    - "online visual analytics of text streams"

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.HC(Human-Computer Interaction)