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: "gradient regularization improves accuracy of discriminative models"
  Abstract: Regularizing the gradient norm of the output of a neural network with respect to its inputs is a powerful technique, first proposed by Drucker & LeCun (1991) who named it Double Backpropagation. The idea has been independently rediscovered several times since then, most often with the goal of making models robust against adversarial sampling. This paper presents evidence that gradient regularization can consistently and significantly improve classification accuracy on vision tasks, especially when the amount of training data is small. We introduce our regularizers as members of a broader class of Jacobian-based regularizers, and compare them theoretically and empirically. #R##N#A straightforward objection against minimizing the gradient norm at the training points is that a locally optimal solution, where the model has small gradients at the training points, may possibly contain large changes at other regions. We demonstrate through experiments on real and synthetic tasks that stochastic gradient descent is unable to find these locally optimal but globally unproductive solutions. Instead, it is forced to find solutions that generalize well.

Connected through hyperedge HE-1 (induced by source paper "virtual mixup training for unsupervised domain adaptation"):
  with members:
    - "mixmatch a holistic approach to semi supervised learning"
    - "sample to sample correspondence for unsupervised domain adaptation"
    - "unpaired multi domain image generation via regularized conditional gans"
    - "co regularized alignment for unsupervised domain adaptation"
    - "mixup as locally linear out of manifold regularization"
    - "image to image translation for domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "maximum classifier discrepancy for unsupervised domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "regularizing deep multi task networks using orthogonal gradients"):
  with members:
    - "meta learning representations for continual learning"
    - "which tasks should be learned together in multi task learning"
    - "on the variance of the adaptive learning rate and beyond"
    - "branched multi task networks deciding what layers to share"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "taskonomy disentangling task transfer learning"
    - "end to end multi task learning with attention"
    - "multi task learning as multi objective optimization"

Connected through hyperedge HE-3 (induced by source paper "minimal achievable sufficient statistic learning"):
  with members:
    - "learning representations for neural network based classification using the information bottleneck principle"
    - "uncertainty in the variational information bottleneck"
    - "estimating information flow in neural networks"
    - "improving dnn robustness to adversarial attacks using jacobian regularization"
    - "emergence of invariance and disentanglement in deep representations"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "opening the black box of deep neural networks via information"
    - "nonlinear information bottleneck"

Connected through hyperedge HE-4 (induced by source paper "improving noise tolerance of mixed signal neural networks"):
  with members:
    - "high accuracy inference in neuromorphic circuits using hardware aware training"
    - "nice noise injection and clamping estimation for neural network quantization"
    - "pact parameterized clipping activation for quantized neural networks"
    - "wrpn wide reduced precision networks"
    - "towards end to end speech recognition with deep convolutional neural networks"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "deep neural networks are robust to weight binarization and other non linear distortions"
    - "xnor net imagenet classification using binary convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "why do adversarial attacks transfer explaining transferability of evasion and poisoning attacks"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "is feature selection secure against training data poisoning"
    - "black box adversarial attacks with limited queries and information"
    - "stealing hyperparameters in machine learning"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "yes machine learning can be more secure a case study on android malware detection"

Connected through hyperedge HE-6 (induced by source paper "improving dnn robustness to adversarial attacks using jacobian regularization"):
  with members:
    - "fix your classifier the marginal value of training the last weight layer"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "classification regions of deep neural networks"
    - "magnet a two pronged defense against adversarial examples"
    - "safetynet detecting and rejecting adversarial examples robustly"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "analysis of universal adversarial perturbations"
    - "adversarial image perturbation for privacy protection a game theory perspective"

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)