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 explanation of dnn based prediction with guided feature inversion"
  Abstract: While deep neural networks (DNN) have become an effective computational tool, the prediction results are often criticized by the lack of interpretability, which is essential in many real-world applications such as health informatics. Existing attempts based on local interpretations aim to identify relevant features contributing the most to the prediction of DNN by monitoring the neighborhood of a given input. They usually simply ignore the intermediate layers of the DNN that might contain rich information for interpretation. To bridge the gap, in this paper, we propose to investigate a guided feature inversion framework for taking advantage of the deep architectures towards effective interpretation. The proposed framework not only determines the contribution of each feature in the input but also provides insights into the decision-making process of DNN models. By further interacting with the neuron of the target category at the output layer of the DNN, we enforce the interpretation result to be class-discriminative. We apply the proposed interpretation model to different CNN architectures to provide explanations for image data and conduct extensive experiments on ImageNet and PASCAL VOC07 datasets. The interpretation results demonstrate the effectiveness of our proposed framework in providing class-discriminative interpretation for DNN-based prediction.

Connected through hyperedge HE-1 (induced by source paper "fda feature disruptive attack"):
  with members:
    - "techniques for interpretable machine learning"
    - "adversarial logit pairing"
    - "gray box adversarial training"
    - "deflecting adversarial attacks with pixel deflection"
    - "generalizable data free objective for crafting universal adversarial perturbations"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "progressive neural architecture search"
    - "defense against adversarial attacks using high level representation guided denoiser"

Connected through hyperedge HE-2 (induced by source paper "fairness in deep learning a computational perspective"):
  with members:
    - "learning credible deep neural networks with rationale regularization"
    - "auditing imagenet towards a model driven framework for annotating demographic attributes of large scale image datasets"
    - "putting fairness principles into practice challenges metrics and improvements"
    - "incorporating priors with feature attribution on text classification"
    - "deep learning for face recognition pride or prejudiced"
    - "on attribution of recurrent neural network predictions via additive decomposition"
    - "compositional fairness constraints for graph embeddings"
    - "flexibly fair representation learning by disentanglement"

Connected through hyperedge HE-3 (induced by source paper "interpreting adversarial examples with attributes"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "one pixel attack for fooling deep neural networks"
    - "on evaluating adversarial robustness"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "a large scale attribute dataset for zero shot learning"
    - "techniques for interpretable machine learning"
    - "defense gan protecting classifiers against adversarial attacks using generative models"

Connected through hyperedge HE-4 (induced by source paper "learning credible deep neural networks with rationale regularization"):
  with members:
    - "on attribution of recurrent neural network predictions via additive decomposition"
    - "evaluating explanation without ground truth in interpretable machine learning"
    - "investigating backtranslation in neural machine translation"
    - "women also snowboard overcoming bias in captioning models"
    - "annotation artifacts in natural language inference data"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "techniques for interpretable machine learning"
    - "know what you don t know unanswerable questions for squad"

Connected through hyperedge HE-5 (induced by source paper "score cam improved visual explanations via score weighted class activation mapping"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "interpretable and fine grained visual explanations for convolutional neural networks"
    - "smooth grad cam an enhanced inference level visualization technique for deep convolutional neural network models"
    - "u cam visual explanation using uncertainty based class activation maps"
    - "techniques for interpretable machine learning"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "explaining image classifiers by counterfactual generation"
    - "rise randomized input sampling for explanation of black box models"

Connected through hyperedge HE-6 (induced by source paper "evaluating explanation without ground truth in interpretable machine learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "an evaluation of the human interpretability of explanation"
    - "learning interpretable models with causal guarantees"
    - "interpretable deep learning in drug discovery"
    - "on attribution of recurrent neural network predictions via additive decomposition"
    - "neural network attributions a causal perspective"
    - "on the in fidelity and sensitivity for explanations"
    - "interpreting neural networks using flip points"

Connected through hyperedge HE-7 (induced by source paper "interpretable and fine grained visual explanations for convolutional neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "visual interpretability for deep learning a survey"
    - "regional multi scale approach for visually pleasing explanations of deep neural networks"
    - "techniques for interpretable machine learning"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "a theoretical explanation for perplexing behaviors of backpropagation based visualizations"
    - "explaining image classifiers by counterfactual generation"
    - "rise randomized input sampling for explanation of black box models"

Connected through hyperedge HE-8 (induced by source paper "understanding misclassifications by attributes"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "one pixel attack for fooling deep neural networks"
    - "interpreting adversarially trained convolutional neural networks"
    - "counterfactual visual explanations"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "a large scale attribute dataset for zero shot learning"
    - "techniques for interpretable machine learning"

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)