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: "efficient training for positive unlabeled learning"
  Abstract: Positive unlabeled (PU) learning is useful in various practical situations, where there is a need to learn a classifier for a class of interest from an unlabeled data set, which may contain anomalies as well as samples from unknown classes. The learning task can be formulated as an optimization problem under the framework of statistical learning theory. Recent studies have theoretically analyzed its properties and generalization performance, nevertheless, little effort has been made to consider the problem of scalability, especially when large sets of unlabeled data are available. In this work we propose a novel scalable PU learning algorithm that is theoretically proven to provide the optimal solution, while showing superior computational and memory performance. Experimental evaluation confirms the theoretical evidence and shows that the proposed method can be successfully applied to a large variety of real-world problems involving PU learning.

Connected through hyperedge HE-1 (induced by source paper "deep weakly supervised anomaly detection"):
  with members:
    - "deep anomaly detection with deviation networks"
    - "deep semi supervised anomaly detection"
    - "adversarially learned anomaly detection"
    - "learning representations of ultrahigh dimensional data for random distance based outlier detection"
    - "one class adversarial nets for fraud detection"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "prototypical networks for few shot learning"
    - "the effectiveness of data augmentation in image classification using deep learning"

Connected through hyperedge HE-2 (induced by source paper "on exploring undetermined relationships for visual relationship detection"):
  with members:
    - "shuffle then assemble learning object agnostic visual relationship features"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "vip cnn visual phrase guided convolutional neural network"
    - "detecting and recognizing human object interactions"
    - "neural motifs scene graph parsing with global context"
    - "relation networks for object detection"
    - "detecting visual relationships with deep relational networks"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"

Connected through hyperedge HE-3 (induced by source paper "generating relevant counter examples from a positive unlabeled dataset for image classification"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "generalizable adversarial training via spectral normalization"
    - "dropout gan learning from a dynamic ensemble of discriminators"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "progressive growing of gans for improved quality stability and variation"
    - "positive unlabeled learning with non negative risk estimator"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-4 (induced by source paper "deep anomaly detection with deviation networks"):
  with members:
    - "adversarially learned anomaly detection"
    - "learning representations of ultrahigh dimensional data for random distance based outlier detection"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-5 (induced by source paper "a method on selecting reliable samples based on fuzziness in positive and unlabeled learning"):
  with members:
    - "theoretical comparisons of positive unlabeled learning against positive negative learning"

Connected through hyperedge HE-6 (induced by source paper "positive unlabeled learning with non negative risk estimator"):
  with members:
    - "loss factorization weakly supervised learning and label noise robustness"
    - "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"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "pu learning for matrix completion"
    - "striving for simplicity the all convolutional net"

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)