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: "improved semi supervised learning with gans using manifold invariances"
  Abstract: Semi-supervised learning methods using Generative Adversarial Networks (GANs) have shown promising empirical success recently. Most of these methods use a shared discriminator/classifier which discriminates real examples from fake while also predicting the class label. Motivated by the ability of the GANs generator to capture the data manifold well, we propose to estimate the tangent space to the data manifold using GANs and employ it to inject invariances into the classifier. In the process, we propose enhancements over existing methods for learning the inverse mapping (i.e., the encoder) which greatly improves in terms of semantic similarity of the reconstructed sample with the input sample. We observe considerable empirical gains in semi-supervised learning over baselines, particularly in the cases when the number of labeled examples is low. We also provide insights into how fake examples influence the semi-supervised learning procedure.

Connected through hyperedge HE-1 (induced by source paper "transferring knowledge across learning processes"):
  with members:
    - "on first order meta learning algorithms"
    - "understanding short horizon bias in stochastic meta optimization"
    - "a coordinate free construction of scalable natural gradient"
    - "differentiable plasticity training plastic neural networks with backpropagation"
    - "life long disentangled representation learning with cross domain latent homologies"
    - "exploring the limits of weakly supervised pretraining"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "progressive neural networks"

Connected through hyperedge HE-2 (induced by source paper "manifold regularization with gans for semi supervised learning"):
  with members:
    - "which training methods for gans do actually converge"
    - "good semi supervised learning that requires a bad gan"
    - "the numerics of gans"
    - "triple generative adversarial nets"
    - "improved training of wasserstein gans"
    - "the riemannian geometry of deep generative models"
    - "improved techniques for training gans"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "generative visual rationales"):
  with members:
    - "self normalizing neural networks"
    - "improved training of wasserstein gans"
    - "generating visual explanations"
    - "why should i trust you explaining the predictions of any classifier"
    - "learning deep features for discriminative localization"
    - "visualizing and understanding convolutional networks"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-4 (induced by source paper "learning loss functions for semi supervised learning via discriminative adversarial networks"):
  with members:
    - "attention over attention neural networks for reading comprehension"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "bilateral multi perspective matching for natural language sentences"
    - "attentive pooling networks"
    - "improved techniques for training gans"
    - "iterative alternating neural attention for machine reading"
    - "image to image translation with conditional adversarial networks"
    - "a connection between generative adversarial networks inverse reinforcement learning and energy based models"

Connected through hyperedge HE-5 (induced by source paper "semi supervised learning with ipm based gans an empirical study"):
  with members:
    - "mcgan mean and covariance feature matching gan"
    - "good semi supervised learning that requires a bad gan"
    - "mmd gan towards deeper understanding of moment matching network"
    - "improved training of wasserstein gans"
    - "temporal ensembling for semi supervised learning"
    - "improved techniques for training gans"
    - "least squares generative adversarial networks"
    - "normalizing the normalizers comparing and extending network normalization schemes"

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)