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: "inverting visual representations with convolutional networks"
  Abstract: Feature representations, both hand-designed and learned ones, are often hard to analyze and interpret, even when they are extracted from visual data. We propose a new approach to study image representations by inverting them with an up-convolutional neural network. We apply the method to shallow representations (HOG, SIFT, LBP), as well as to deep networks. For shallow representations our approach provides significantly better reconstructions than existing methods, revealing that there is surprisingly rich information contained in these features. Inverting a deep network trained on ImageNet provides several insights into the properties of the feature representation learned by the network. Most strikingly, the colors and the rough contours of an image can be reconstructed from activations in higher network layers and even from the predicted class probabilities.

Connected through hyperedge HE-1 (induced by source paper "interpretable cnns for object classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "one pixel attack for fooling deep neural networks"
    - "visual interpretability for deep learning a survey"
    - "explaining neural networks semantically and quantitatively"
    - "network dissection quantifying interpretability of deep visual representations"
    - "interactively transferring cnn patterns for part localization"
    - "explaining the unexplained a class enhanced attentive response clear approach to understanding deep neural networks"
    - "visual explanations for convolutional neural networks via input resampling"

Connected through hyperedge HE-2 (induced by source paper "vec2face unveil human faces from their blackbox features in face recognition"):
  with members:
    - "black box adversarial attacks with bayesian optimization"
    - "shrinkteanet million scale lightweight face recognition via shrinking teacher student networks"
    - "adacos adaptively scaling cosine logits for effectively learning deep face representations"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "arcface additive angular margin loss for deep face recognition"
    - "learning towards minimum hyperspherical energy"
    - "black box adversarial attacks with limited queries and information"
    - "constructing unrestricted adversarial examples with generative models"

Connected through hyperedge HE-3 (induced by source paper "explaining knowledge distillation by quantifying the knowledge"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "similarity of neural network representations revisited"
    - "quantifying layerwise information discarding of neural networks"
    - "stiffness a new perspective on generalization in neural networks"
    - "transferring knowledge across learning processes"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "understanding training and generalization in deep learning by fourier analysis"
    - "i revnet deep invertible networks"

Connected through hyperedge HE-4 (induced by source paper "adapting grad cam for embedding networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "hardness aware deep metric learning"
    - "visualizing how embeddings generalize"
    - "improved embeddings with easy positive triplet mining"
    - "divide and conquer the embedding space for metric learning"
    - "visual explanation for deep metric learning"
    - "visualizing deep similarity networks"
    - "deep metric learning beyond binary supervision"

Connected through hyperedge HE-5 (induced by source paper "privacy preserving learning via deep net pruning"):
  with members:
    - "deep leakage from gradients"
    - "stronger l2 l2 compressed sensing without iterating"
    - "inverting deep generative models one layer at a time"
    - "nearly sample optimal sparse fourier transform in any dimension ripless and filterless"
    - "the state of sparsity in deep neural networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "federated learning strategies for improving communication efficiency"
    - "split learning for health distributed deep learning without sharing raw patient data"

Connected through hyperedge HE-6 (induced by source paper "examining the benefits of capsule neural networks"):
  with members:
    - "convolutional capsule network for classification of breast cancer histology images"
    - "videocapsulenet a simplified network for action detection"
    - "fast capsnet for lung cancer screening"
    - "brain tumor type classification via capsule networks"
    - "improved explainability of capsule networks relevance path by agreement"
    - "novel deep learning model for traffic sign detection using capsule networks"
    - "deep reinforcement learning using capsules in advanced game environments"
    - "compositional coding capsule network with k means routing for text classification"

Connected through hyperedge HE-7 (induced by source paper "deep quaternion features for privacy protection"):
  with members:
    - "adversarial neural network inversion via auxiliary knowledge alignment"
    - "complex valued neural networks for privacy protection"
    - "interpretable complex valued neural networks for privacy protection"
    - "quaternion convolutional neural networks"
    - "privynet a flexible framework for privacy preserving deep neural network training"
    - "exploiting unintended feature leakage in collaborative learning"
    - "gazelle a low latency framework for secure neural network inference"
    - "not just privacy improving performance of private deep learning in mobile cloud"

Connected through hyperedge HE-8 (induced by source paper "a survey on explainable artificial intelligence xai towards medical xai"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "interpretable neural networks for predicting mortality risk using multi modal electronic health records"
    - "an evaluation of the human interpretability of explanation"
    - "protoattend attention based prototypical learning"
    - "understanding the un interpretability of natural image distributions using generative models"
    - "outlining the design space of explainable intelligent systems for medical diagnosis"
    - "chexpert a large chest radiograph dataset with uncertainty labels and expert comparison"
    - "interpretable dynamics models for data efficient reinforcement 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.NE(Neural and Evolutionary Computing)