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: "bidirectional learning for robust neural networks"
  Abstract: A multilayer perceptron can behave as a generative classifier by applying bidirectional learning (BL). It consists of training an undirected neural network to map input to output and vice-versa; therefore it can produce a classifier in one direction, and a generator in the opposite direction for the same data. In this paper, two novel learning techniques are introduced which use BL for improving robustness to white noise static and adversarial examples. The first method is bidirectional propagation of errors, which the error propagation occurs in backward and forward directions. Motivated by the fact that its generative model receives as input a constant vector per class, we introduce as a second method the hybrid adversarial networks (HAN). Its generative model receives a random vector as input and its training is based on generative adversarial networks (GAN). To assess the performance of BL, we perform experiments using several architectures with fully and convolutional layers, with and without bias. Experimental results show that both methods improve robustness to white noise static and adversarial examples, but have different behaviour depending on the architecture and task, being more beneficial to use the one or the other. Nevertheless, HAN using a convolutional architecture with batch normalization presents outstanding robustness, reaching state-of-the-art accuracy on adversarial examples of hand-written digits.

Connected through hyperedge HE-1 (induced by source paper "bidirectional learning for domain adaptation of semantic segmentation"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "multimodal unsupervised image to image translation"
    - "boosting domain adaptation by discovering latent domains"
    - "domain stylization a strong simple baseline for synthetic to real image domain adaptation"
    - "effective use of synthetic data for urban scene semantic segmentation"
    - "taking a closer look at domain shift category level adversaries for semantics consistent domain adaptation"
    - "bi directional neural machine translation with synthetic parallel data"

Connected through hyperedge HE-2 (induced by source paper "weight map layer for noise and adversarial attack robustness"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "gradient adversarial training of neural networks"
    - "extending defensive distillation"
    - "hypernetworks with statistical filtering for defending adversarial examples"
    - "show attend and tell neural image caption generation with visual attention"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "grammar as a foreign language"

Connected through hyperedge HE-3 (induced by source paper "motivating the rules of the game for adversarial example research"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "machine vs machine minimax optimal defense against adversarial examples"
    - "scaling provable adversarial defenses"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "stochastic activation pruning for robust adversarial defense"
    - "adversarial patch"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "adversarial examples that fool both computer vision and time limited humans"

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)