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: "exact and consistent interpretation for piecewise linear neural networks a closed form solution"
  Abstract: Strong intelligent machines powered by deep neural networks are increasingly deployed as black boxes to make decisions in risk-sensitive domains, such as finance and medical. To reduce potential risk and build trust with users, it is critical to interpret how such machines make their decisions. Existing works interpret a pre-trained neural network by analyzing hidden neurons, mimicking pre-trained models or approximating local predictions. However, these methods do not provide a guarantee on the exactness and consistency of their interpretation. In this paper, we propose an elegant closed form solution named $OpenBox$ to compute exact and consistent interpretations for the family of Piecewise Linear Neural Networks (PLNN). The major idea is to first transform a PLNN into a mathematically equivalent set of linear classifiers, then interpret each linear classifier by the features that dominate its prediction. We further apply $OpenBox$ to demonstrate the effectiveness of non-negative and sparse constraints on improving the interpretability of PLNNs. The extensive experiments on both synthetic and real world data sets clearly demonstrate the exactness and consistency of our interpretation.

Connected through hyperedge HE-1 (induced by source paper "analytic marching an analytic meshing solution from deep implicit surface networks"):
  with members:
    - "orthogonal deep neural networks"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "deep level sets implicit surface representations for 3d shape inference"
    - "blessing of dimensionality mathematical foundations of the statistical physics of data"
    - "thinet a filter level pruning method for deep neural network compression"
    - "learning both weights and connections for efficient neural networks"
    - "shapenet an information rich 3d model repository"
    - "on the number of response regions of deep feed forward networks with piece wise linear activations"

Connected through hyperedge HE-2 (induced by source paper "a multidisciplinary survey and framework for design and evaluation of explainable ai systems"):
  with members:
    - "fairsight visual analytics for fairness in decision making"
    - "predicting model failure using saliency maps in autonomous driving systems"
    - "human grounded evaluations of explanation methods for text classification"
    - "quantifying interpretability and trust in machine learning systems"
    - "summit scaling deep learning interpretability by visualizing activation and attribution summarizations"
    - "explainable artificial intelligence xai concepts taxonomies opportunities and challenges toward responsible ai"
    - "open issues in combating fake news interpretability as an opportunity"
    - "a survey on bias and fairness in machine learning"

Connected through hyperedge HE-3 (induced by source paper "exploring interpretable lstm neural networks over multi variable data"):
  with members:
    - "fine grained attention mechanism for neural machine translation"
    - "an interpretable lstm neural network for autoregressive exogenous model"
    - "raim recurrent attentive and intensive model of multimodal patient monitoring data"
    - "deep multi output forecasting learning to accurately predict blood glucose trajectories"
    - "methods for interpreting and understanding deep neural networks"
    - "multilevel wavelet decomposition network for interpretable time series analysis"
    - "beyond word importance contextual decomposition to extract interactions from lstms"
    - "a dual stage attention based recurrent neural network for time series prediction"

Connected through hyperedge HE-4 (induced by source paper "on attribution of recurrent neural network predictions via additive decomposition"):
  with members:
    - "towards explanation of dnn based prediction with guided feature inversion"
    - "evaluating neural network explanation methods using hybrid documents and morphological prediction"
    - "techniques for interpretable machine learning"
    - "deep contextualized word representations"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "interpretable adversarial perturbation in input embedding space for text"
    - "beyond word importance contextual decomposition to extract interactions from lstms"

Connected through hyperedge HE-5 (induced by source paper "exact and consistent interpretation of piecewise linear models hidden behind apis a closed form solution"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "explaining deep learning models a bayesian non parametric approach"
    - "techniques for interpretable machine learning"
    - "smoothgrad removing noise by adding noise"
    - "interpreting deep visual representations via network dissection"
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"
    - "towards better understanding of gradient based attribution methods for deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "multi granularity reasoning for social relation recognition from images"):
  with members:
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "simple baselines for human pose estimation and tracking"
    - "a domain based approach to social relation recognition"
    - "dual glance model for deciphering social relationships"
    - "mask r cnn"
    - "semantic object parsing with graph lstm"
    - "from facial expression recognition to interpersonal relation prediction"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-7 (induced by source paper "transparent classification with multilayer logical perceptrons and random binarization"):
  with members:
    - "learning algorithms via neural logic networks"
    - "interpreting cnns via decision trees"
    - "interpreting deep visual representations via network dissection"
    - "interpretable convolutional neural networks"
    - "distilling a neural network into a soft decision tree"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "scalable bayesian rule lists"
    - "the mythos of model interpretability"

Connected through hyperedge HE-8 (induced by source paper "a survey of safety and trustworthiness of deep neural networks"):
  with members:
    - "analyzing deep neural networks with symbolic propagation towards higher precision and faster verification"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "nn dependability kit engineering neural networks for safety critical systems"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "towards dependability metrics for neural networks"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"

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)