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: "on the decision boundary of deep neural networks"
  Abstract: While deep learning models and techniques have achieved great empirical success, our understanding of the source of success in many aspects remains very limited. In an attempt to bridge the gap, we investigate the decision boundary of a production deep learning architecture with weak assumptions on both the training data and the model. We demonstrate, both theoretically and empirically, that the last weight layer of a neural network converges to a linear SVM trained on the output of the last hidden layer, for both the binary case and the multi-class case with the commonly used cross-entropy loss. Furthermore, we show empirically that training a neural network as a whole, instead of only fine-tuning the last weight layer, may result in better bias constant for the last weight layer, which is important for generalization. In addition to facilitating the understanding of deep learning, our result can be helpful for solving a broad range of practical problems of deep learning, such as catastrophic forgetting and adversarial attacking. The experiment codes are available at this https URL

Connected through hyperedge HE-1 (induced by source paper "hold me tight influence of discriminative features on deep network boundaries"):
  with members:
    - "on the effectiveness of low frequency perturbations"
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "a fourier perspective on model robustness in computer vision"
    - "pytorch an imperative style high performance deep learning library"
    - "robustness via curvature regularization and vice versa"
    - "adversarial spheres"
    - "on the structural sensitivity of deep convolutional networks to the directions of fourier basis functions"
    - "with friends like these who needs adversaries"

Connected through hyperedge HE-2 (induced by source paper "neural language priors"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "improving tree lstm with tree attention"
    - "no training required exploring random encoders for sentence classification"
    - "universal transformers"
    - "weighted transformer network for machine translation"
    - "self attention with relative position representations"
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "universal sentence encoder"

Connected through hyperedge HE-3 (induced by source paper "a tour of convolutional networks guided by linear interpreters"):
  with members:
    - "towards explanation of dnn based prediction with guided feature inversion"
    - "visual interpretability for deep learning a survey"
    - "prediction error meta classification in semantic segmentation detection via aggregated dispersion measures of softmax probabilities"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "understanding deep architectures by visual summaries"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"

Connected through hyperedge HE-4 (induced by source paper "characterizing the decision boundary of deep neural networks"):
  with members:
    - "deep adversarial social recommendation"
    - "learning hierarchical discourse level structure for fake news detection"
    - "deep adversarial network alignment"
    - "on the decision boundaries of deep neural networks a tropical geometry perspective"
    - "adversarial attacks and defenses in images graphs and text a review"
    - "investigating decision boundaries of trained neural networks"
    - "pytorch an imperative style high performance deep learning library"
    - "adversarial attacks on deep learning models in natural language processing a survey"

Connected through hyperedge HE-5 (induced by source paper "on the decision boundaries of deep neural networks a tropical geometry perspective"):
  with members:
    - "on the geometry of adversarial examples"
    - "on decision regions of narrow deep neural networks"
    - "robustness via curvature regularization and vice versa"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "tropical geometry of deep neural networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "structured adversarial attack towards general implementation and better interpretability"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-6 (induced by source paper "accelerating flash calculation through deep learning methods"):
  with members:
    - "dlbi deep learning guided bayesian inference for structure reconstruction of super resolution fluorescence microscopy"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "supportnet solving catastrophic forgetting in class incremental learning with support data"):
  with members:
    - "progressive neural networks"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "continual learning with deep generative replay"
    - "overcoming catastrophic forgetting by incremental moment matching"
    - "measuring catastrophic forgetting in neural networks"
    - "gradient episodic memory for continual learning"
    - "sgd learns over parameterized networks that provably generalize on linearly separable data"
    - "less forgetting learning in deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "collaboratively weighting deep and classic representation via l2 regularization for image classification"):
  with members:
    - "random erasing data augmentation"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "learning structured sparsity in deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "wide residual networks"
    - "deep residual learning for image recognition"
    - "learning locality constrained collaborative representation for robust face recognition"

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)