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: "improving transparency of deep neural inference process"
  Abstract: Deep learning techniques are rapidly advanced recently, and becoming a necessity component for widespread systems. However, the inference process of deep learning is black-box, and not very suitable to safety-critical systems which must exhibit high transparency. In this paper, to address this black-box limitation, we develop a simple analysis method which consists of 1) structural feature analysis: lists of the features contributing to inference process, 2) linguistic feature analysis: lists of the natural language labels describing the visual attributes for each feature contributing to inference process, and 3) consistency analysis: measuring consistency among input data, inference (label), and the result of our structural and linguistic feature analysis. Our analysis is simplified to reflect the actual inference process for high transparency, whereas it does not include any additional black-box mechanisms such as LSTM for highly human readable results. We conduct experiments and discuss the results of our analysis qualitatively and quantitatively, and come to believe that our work improves the transparency of neural networks. Evaluated through 12,800 human tasks, 75% workers answer that input data and result of our feature analysis are consistent, and 70% workers answer that inference (label) and result of our feature analysis are consistent. In addition to the evaluation of the proposed analysis, we find that our analysis also provide suggestions, or possible next actions such as expanding neural network complexity or collecting training data to improve a neural network.

Connected through hyperedge HE-1 (induced by source paper "engineering problems in machine learning systems"):
  with members:
    - "towards dependability metrics for neural networks"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "runtime monitoring neuron activation patterns"
    - "quantitative projection coverage for testing ml enabled autonomous systems"
    - "towards proving the adversarial robustness of deep neural networks"
    - "maximum resilience of artificial neural networks"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "an analysis of iso 26262 using machine learning safely in automotive software"

Connected through hyperedge HE-2 (induced by source paper "intentional attention mask transformation for robust cnn classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "interpretation of feature space using multi channel attentional sub networks"
    - "visual interpretability for deep learning a survey"
    - "attention branch network learning of attention mechanism for visual explanation"
    - "squeeze and excitation networks"
    - "residual attention network for image classification"
    - "multi scale context aggregation by dilated convolutions"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-3 (induced by source paper "interpretation of feature space using multi channel attentional sub networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "weighted sigmoid gate unit for an activation function of deep neural network"
    - "visual interpretability for deep learning a survey"
    - "attention branch network learning of attention mechanism for visual explanation"
    - "multi scale context aggregation by dilated convolutions"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-4 (induced by source paper "adapting square for quality assessment of artificial intelligence systems"):
  with members:
    - "machine learning testing survey landscapes and horizons"
    - "towards a framework to manage perceptual uncertainty for safe automated driving"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "why should i trust you explaining the predictions of any classifier"

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.LG(Machine Learning)