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: "this looks like that deep learning for interpretable image recognition"
  Abstract: When we are faced with challenging image classification tasks, we often explain our reasoning by dissecting the image, and pointing out prototypical aspects of one class or another. The mounting evidence for each of the classes helps us make our final decision. In this work, we introduce a deep network architecture that reasons in a similar way: the network dissects the image by finding prototypical parts, and combines evidence from the prototypes to make a final classification. The algorithm thus reasons in a way that is qualitatively similar to the way ornithologists, physicians, geologists, architects, and others would explain to people on how to solve challenging image classification tasks. The network uses only image-level labels for training, meaning that there are no labels for parts of images. We demonstrate the method on the CIFAR-10 dataset and 10 classes from the CUB-200-2011 dataset.

Connected through hyperedge HE-1 (induced by source paper "concept whitening for interpretable image recognition"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "the convolutional tsetlin machine"
    - "educe explaining model decisions through unsupervised concepts extraction"
    - "saccader improving accuracy of hard attention models for vision"
    - "cheap orthogonal constraints in neural networks a simple parametrization of the orthogonal and unitary group"
    - "on concept based explanations in deep neural networks"
    - "iterative normalization beyond standardization towards efficient whitening"
    - "sanity checks for saliency maps"

Connected through hyperedge HE-2 (induced by source paper "bayes probe distribution guided sampling for prediction level sets"):
  with members:
    - "fooling lime and shap adversarial attacks on post hoc explanation methods"
    - "out of distribution detection in classifiers via generation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "pyro deep universal probabilistic programming"
    - "sim to real learning agile locomotion for quadruped robots"
    - "certified adversarial robustness with additive noise"
    - "sanity checks for saliency maps"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "right for the wrong scientific reasons revising deep networks by interacting with their explanations"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "deep learning predicts hip fracture using confounding patient and healthcare variables"
    - "explaining explanations an overview of interpretability of machine learning"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "variable generalization performance of a deep learning model to detect pneumonia in chest radiographs a cross sectional study"
    - "sanity checks for saliency maps"

Connected through hyperedge HE-4 (induced by source paper "explainable deep modeling of tabular data using tablegraphnet"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "deep sets"
    - "relational inductive biases deep learning and graph networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"
    - "interpretable convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "black box explanation by learning image exemplars in the latent feature space"):
  with members:
    - "enhancing the robustness of deep neural networks by boundary conditional gan"
    - "local rule based explanations of black box decision systems"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "distilling a neural network into a soft decision tree"
    - "not just a black box learning important features through propagating activation differences"
    - "why should i trust you explaining the predictions of any classifier"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "understanding artificial intelligence ethics and safety"):
  with members:
    - "a roadmap toward the resilient internet of things for cyber physical systems"
    - "a grounded interaction protocol for explainable artificial intelligence"
    - "automated rationale generation a technique for explainable ai and its effects on human perceptions"
    - "model cards for model reporting"
    - "a framework for understanding unintended consequences of machine learning"
    - "spectrum data poisoning with adversarial deep learning"
    - "explaining explanations in ai"
    - "problem formulation and fairness"

Connected through hyperedge HE-7 (induced by source paper "ai enabling technologies a survey"):
  with members:
    - "the lustre storage architecture"
    - "qiniu submission to activitynet challenge 2018"
    - "moments in time dataset one million videos for event understanding"
    - "interactive supercomputing on 40 000 cores for machine learning and data analysis"
    - "gazelle a low latency framework for secure neural network inference"
    - "tracking emerges by colorizing videos"
    - "robust physical world attacks on deep learning models"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-8 (induced by source paper "protoattend attention based prototypical learning"):
  with members:
    - "on the robustness of deep k nearest neighbors"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "representer point selection for explaining deep neural networks"
    - "deep anomaly detection with outlier exposure"
    - "catboost gradient boosting with categorical features support"
    - "incremental few shot learning with attention attractor networks"
    - "attention based ensemble for deep metric learning"
    - "bayesian 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.LG(Machine Learning)