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: "towards better understanding of gradient based attribution methods for deep neural networks"
  Abstract: Understanding the flow of information in Deep Neural Networks (DNNs) is a challenging problem that has gain increasing attention over the last few years. While several methods have been proposed to explain network predictions, there have been only a few attempts to compare them from a theoretical perspective. What is more, no exhaustive empirical comparison has been performed in the past. In this work, we analyze four gradient-based attribution methods and formally prove conditions of equivalence and approximation between them. By reformulating two of these methods, we construct a unified framework which enables a direct comparison, as well as an easier implementation. Finally, we propose a novel evaluation metric, called Sensitivity-n and test the gradient-based attribution methods alongside with a simple perturbation-based attribution method on several datasets in the domains of image and text classification, using various network architectures.

Connected through hyperedge HE-1 (induced by source paper "explaining explanations axiomatic feature interactions for deep networks"):
  with members:
    - "proxy tasks and subjective measures can be misleading in evaluating explainable ai systems"
    - "segment integrated gradients better attributions through regions"
    - "on identifiability in transformers"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "learning pairwise interactions with bayesian neural networks"
    - "incorporating priors with feature attribution on text classification"
    - "learning explainable models using attribution priors"
    - "is attention interpretable"

Connected through hyperedge HE-2 (induced by source paper "sam the sensitivity of attribution methods to hyperparameters"):
  with members:
    - "interpretable and fine grained visual explanations for convolutional neural networks"
    - "towards explainable artificial intelligence"
    - "visualizing deep networks by optimizing with integrated gradients"
    - "removing input features via a generative model to explain their attributions to classifier s decisions"
    - "methods for interpreting and understanding deep neural networks"
    - "understanding deep architectures by visual summaries"
    - "interpreting and explaining deep neural networks for classification of audio signals"
    - "explaining image classifiers by counterfactual generation"

Connected through hyperedge HE-3 (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-4 (induced by source paper "aggregating explanation methods for stable and robust explainability"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "fairwashing the risk of rationalization"
    - "fooling neural network interpretations via adversarial model manipulation"
    - "interpretable deep learning under fire"
    - "a human grounded evaluation benchmark for local explanations of machine learning"
    - "explaining image classifiers by counterfactual generation"
    - "sanity checks for saliency maps"
    - "smoothgrad removing noise by adding noise"

Connected through hyperedge HE-5 (induced by source paper "robust saliency maps with decoy enhanced saliency score"):
  with members:
    - "understanding impacts of high order loss approximations and features in deep learning interpretation"
    - "certifiably robust interpretation in deep learning"
    - "interpreting neural networks using flip points"
    - "counterfactual visual explanations"
    - "explaining image classifiers by counterfactual generation"
    - "sanity checks for saliency maps"
    - "learning to explain an information theoretic perspective on model interpretation"
    - "deeppink reproducible feature selection in deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "skd unsupervised keypoint detection for point clouds using saliency estimation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "usip unsupervised stable interest point detection from 3d point clouds"
    - "pcan 3d attention map learning using contextual information for point cloud based retrieval"
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "point voxel cnn for efficient 3d deep learning"
    - "elf embedded localisation of features in pre trained cnn"
    - "deep closest point learning representations for point cloud registration"
    - "ppfnet global context aware local features for robust 3d point matching"

Connected through hyperedge HE-7 (induced by source paper "arae adversarially robust training of autoencoders improves novelty detection"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "deep learning for anomaly detection a survey"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "memorizing normality to detect anomaly memory augmented deep autoencoder for unsupervised anomaly detection"
    - "rare event detection using disentangled representation learning"
    - "real world anomaly detection in surveillance videos"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "adversarially learned one class classifier for novelty detection"

Connected through hyperedge HE-8 (induced by source paper "irof a low resource evaluation metric for explanation methods"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"
    - "why should you trust my explanation understanding uncertainty in lime explanations"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "sanity checks for saliency maps"
    - "evaluating feature importance estimates"
    - "smoothgrad removing noise by adding noise"
    - "axiomatic attribution for deep 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)