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: "generative adversarial positive unlabelled learning"
  Abstract: In this work, we consider the task of classifying binary positive-unlabeled (PU) data. The existing discriminative learning based PU models attempt to seek an optimal reweighting strategy for U data, so that a decent decision boundary can be found. However, given limited P data, the conventional PU models tend to suffer from overfitting when adapted to very flexible deep neural networks. In contrast, we are the first to innovate a totally new paradigm to attack the binary PU task, from perspective of generative learning by leveraging the powerful generative adversarial networks (GAN). Our generative positive-unlabeled (GenPU) framework incorporates an array of discriminators and generators that are endowed with different roles in simultaneously producing positive and negative realistic samples. We provide theoretical analysis to justify that, at equilibrium, GenPU is capable of recovering both positive and negative data distributions. Moreover, we show GenPU is generalizable and closely related to the semi-supervised classification. Given rather limited P data, experiments on both synthetic and real-world dataset demonstrate the effectiveness of our proposed framework. With infinite realistic and diverse sample streams generated from GenPU, a very flexible classifier can then be trained using deep neural networks.

Connected through hyperedge HE-1 (induced by source paper "a variational approach for learning from positive and unlabeled data"):
  with members:
    - "additive margin softmax for face verification"
    - "positive unlabeled learning with non negative risk estimator"
    - "class prior estimation for learning from positive and unlabeled data"
    - "mixture proportion estimation via kernel embedding of distributions"

Connected through hyperedge HE-2 (induced by source paper "revisiting sample selection approach to positive unlabeled learning turning unlabeled data into positive rather than negative"):
  with members:
    - "classification from positive unlabeled and biased negative data"
    - "dimensionality driven learning with noisy labels"
    - "pumpout a meta approach for robustly training deep neural networks with noisy labels"
    - "positive unlabeled learning with non negative risk estimator"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "semi supervised classification based on classification from positive and unlabeled data"
    - "theoretical comparisons of positive unlabeled learning against positive negative learning"
    - "mixture proportion estimation via kernel embedding of distributions"

Connected through hyperedge HE-3 (induced by source paper "punch positive unlabelled classification based information retrieval in hyperspectral images"):
  with members:
    - "ladder networks for semi supervised hyperspectral image classification"
    - "positive unlabeled learning with non negative risk estimator"
    - "bass net band adaptive spectral spatial feature learning neural network for hyperspectral image classification"
    - "unsupervised classification in hyperspectral imagery with nonlocal total variation and primal dual hybrid gradient algorithm"
    - "learning to match using local and distributed representations of text for web search"
    - "deep learning in neural networks"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "advances in hyperspectral image classification earth monitoring with statistical learning methods"

Connected through hyperedge HE-4 (induced by source paper "beyond the selected completely at random assumption for learning from positive and unlabeled data"):
  with members:
    - "learning from positive and unlabeled data a survey"
    - "instance dependent pu learning by bayesian optimal relabeling"
    - "positive unlabeled learning with non negative risk estimator"
    - "class prior estimation for learning from positive and unlabeled data"
    - "unbiased learning to rank with biased feedback"
    - "recommendations as treatments debiasing learning and evaluation"
    - "mixture proportion estimation via kernel embedding of distributions"
    - "one class classification taxonomy of study and review of techniques"

Connected through hyperedge HE-5 (induced by source paper "learning from positive and unlabeled data a survey"):
  with members:
    - "beyond the selected completely at random assumption for learning from positive and unlabeled data"
    - "instance dependent pu learning by bayesian optimal relabeling"
    - "positive unlabeled learning with non negative risk estimator"
    - "scalable demand aware recommendation"
    - "mixture proportion estimation via kernel embedding of distributions"
    - "compositional vector space models for knowledge base completion"
    - "pu learning for matrix completion"

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)