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: "visual explanation by interpretation improving visual feedback capabilities of deep neural networks"
  Abstract: Learning-based representations have become the defacto means to address computer vision tasks. Despite their massive adoption, the amount of work aiming at understanding the internal representations learned by these models is rather limited. Existing methods aimed at model interpretation either require exhaustive manual inspection of visualizations, or link internal network activations with external "possibly useful" annotated concepts. We propose an intermediate scheme in which, given a pretrained model, we automatically identify internal features relevant for the set of classes considered by the model, without requiring additional annotations. We interpret the model through average visualizations of these features. Then, at test time, we explain the network prediction by accompanying the predicted class label with supporting heatmap visualizations derived from the identified relevant features. In addition, we propose a method to address the artifacts introduced by strided operations in deconvnet-based visualizations. Our evaluation on the MNIST, ILSVRC 12 and Fashion 144k datasets quantitatively shows that the proposed method is able to identify relevant internal features for the classes of interest while improving the quality of the produced visualizations.

Connected through hyperedge HE-1 (induced by source paper "towards a unified evaluation of explanation methods without ground truth"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "evaluating explainers via perturbation"
    - "can i trust the explainer verifying post hoc explanatory methods"
    - "bim towards quantitative evaluation of interpretability methods with ground truth"
    - "evaluating recurrent neural network explanations"
    - "evaluating explanation without ground truth in interpretable machine learning"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "towards robust interpretability with self explaining neural networks"

Connected through hyperedge HE-2 (induced by source paper "improving feature attribution through input specific network pruning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "full gradient representation for neural network visualization"
    - "explaining deep neural networks with a polynomial time algorithm for shapley values approximation"
    - "understanding deep networks via extremal perturbations and smooth masks"
    - "interpretable and fine grained visual explanations for convolutional neural networks"
    - "a benchmark for interpretability methods in deep neural networks"
    - "visualizing deep networks by optimizing with integrated gradients"
    - "why are saliency maps noisy cause of and solution to noisy saliency maps"

Connected through hyperedge HE-3 (induced by source paper "generating contrastive explanations with monotonic attribute functions"):
  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"
    - "contrastive explanation a structural model approach"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "interpreting neural network judgments via minimal stable and symbolic corrections"
    - "methods for interpreting and understanding deep neural networks"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "tip typifying the interpretability of procedures"

Connected through hyperedge HE-4 (induced by source paper "model agnostic contrastive explanations for structured data"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "zeroth order stochastic variance reduction for nonconvex optimization"
    - "improving simple models with confidence profiles"
    - "interpreting neural network judgments via minimal stable and symbolic corrections"
    - "methods for interpreting and understanding deep neural networks"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "tip typifying the interpretability of procedures"
    - "interpreting blackbox models via model extraction"

Connected through hyperedge HE-5 (induced by source paper "explanations based on the missing towards contrastive explanations with pertinent negatives"):
  with members:
    - "interpreting neural network judgments via minimal stable and symbolic corrections"
    - "tip typifying the interpretability of procedures"
    - "accountability of ai under the law the role of explanation"
    - "generating natural adversarial examples"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "multifaceted feature visualization uncovering the different types of features learned by each neuron in deep neural networks"
    - "rationalizing neural predictions"
    - "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.CV(Computer Vision and Pattern Recognition)