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: "semi supervised learning with gans manifold invariance with improved inference"
  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 "end to end graph based deep semi supervised learning"):
  with members:
    - "randaugment practical automated data augmentation with a reduced search space"
    - "remixmatch semi supervised learning with distribution alignment and augmentation anchoring"
    - "label propagation for deep semi supervised learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "there are many consistent explanations of unlabeled data why you should average"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised learning via compact latent space clustering"
    - "realistic evaluation of deep semi supervised learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "adversarial based neural networks for affect estimations in the wild"):
  with members:
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "avec 2019 workshop and challenge state of mind detecting depression with ai and cross cultural affect recognition"
    - "end to end facial and physiological model for affective computing and applications"
    - "sewa db a rich database for audio visual emotion and sentiment research in the wild"
    - "deep autoencoder for combined human pose estimation and body model upscaling"
    - "a multi task learning generation framework valence arousal action units primary expressions"
    - "aff wild2 extending the aff wild database for affect recognition"
    - "adversarial training in affective computing and sentiment analysis recent advances and perspectives"

Connected through hyperedge HE-3 (induced by source paper "fast and provable admm for learning with generative priors"):
  with members:
    - "gansynth adversarial neural audio synthesis"
    - "algorithmic aspects of inverse problems using generative models"
    - "memorization precedes generation learning unsupervised gans with memory networks"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "phase retrieval under a generative prior"
    - "deep denoising rate optimal recovery of structured signals with a deep prior"
    - "large scale gan training for high fidelity natural image synthesis"
    - "adversarial audio synthesis"

Connected through hyperedge HE-4 (induced by source paper "nearly minimal over parametrization of shallow neural networks"):
  with members:
    - "quadratic suffices for over parametrization via matrix chernoff bound"
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "an improved analysis of training over parameterized deep neural networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "trainability and data dependent initialization of over parameterized relu neural networks"
    - "on learning over parameterized neural networks a functional approximation prospective"
    - "global convergence of adaptive gradient methods for an over parameterized neural network"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"

Connected through hyperedge HE-5 (induced by source paper "generative discriminative complementary learning"):
  with members:
    - "twin auxiliary classifiers gan"
    - "cgans with projection discriminator"
    - "attention based deep multiple instance learning"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "label noise robust generative adversarial networks"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "good semi supervised learning that requires a bad gan"

Connected through hyperedge HE-6 (induced by source paper "learning condensed and aligned features for unsupervised domain adaptation using label propagation"):
  with members:
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "understanding batch normalization"
    - "domain adaptation for visual applications a comprehensive survey"
    - "domain adaptation using adversarial learning for autonomous navigation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "unsupervised domain adaptation with similarity learning"
    - "adversarial discriminative domain adaptation"
    - "asymmetric tri training for unsupervised domain adaptation"

Connected through hyperedge HE-7 (induced by source paper "lautum regularization for semi supervised transfer learning"):
  with members:
    - "entropy and mutual information in models of deep neural networks"
    - "semi supervised deep kernel learning regression with unlabeled data by minimizing predictive variance"
    - "taskonomy disentangling task transfer learning"
    - "a survey on deep transfer learning"
    - "semi supervised learning with declaratively specified entropy constraints"
    - "generalization error in deep learning"
    - "explicit inductive bias for transfer learning with convolutional networks"
    - "when semi supervised learning meets transfer learning training strategies models and datasets"

Connected through hyperedge HE-8 (induced by source paper "semi supervised learning using adversarial training with good and bad samples"):
  with members:
    - "semi supervised learning based on generative adversarial network a comparison between good gan and bad gan approach"
    - "high fidelity image generation with fewer labels"
    - "spectral normalization for generative adversarial networks"
    - "semi supervised learning with gans revisiting manifold regularization"
    - "large scale gan training for high fidelity natural image synthesis"
    - "semi supervised rare disease detection using generative adversarial network"
    - "triangle generative adversarial networks"
    - "good semi supervised learning that requires a bad gan"

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)