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: "positive unlabeled learning with non negative risk estimator"
  Abstract: From only positive (P) and unlabeled (U) data, a binary classifier could be trained with PU learning, in which the state of the art is unbiased PU learning. However, if its model is very flexible, empirical risks on training data will go negative, and we will suffer from serious overfitting. In this paper, we propose a non-negative risk estimator for PU learning: when getting minimized, it is more robust against overfitting, and thus we are able to use very flexible models (such as deep neural networks) given limited P data. Moreover, we analyze the bias, consistency, and mean-squared-error reduction of the proposed risk estimator, and bound the estimation error of the resulting empirical risk minimizer. Experiments demonstrate that our risk estimator fixes the overfitting problem of its unbiased counterparts.

Connected through hyperedge HE-1 (induced by source paper "object detection as a positive unlabeled problem"):
  with members:
    - "an empirical study of example forgetting during deep neural network learning"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "omnia faster r cnn detection in the wild through dataset merging and soft distillation"
    - "soft sampling for robust object detection"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "deeplesion automated deep mining categorization and detection of significant radiology image findings using large scale clinical lesion annotations"

Connected through hyperedge HE-2 (induced by source paper "does label smoothing mitigate label noise"):
  with members:
    - "regularization via structural label smoothing"
    - "are anchor points really indispensable in label noise learning"
    - "regularized evolution for image classifier architecture search"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "masking a new perspective of noisy supervision"
    - "pumpout a meta approach for robustly training deep neural networks with noisy labels"
    - "on calibration of modern neural networks"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-3 (induced by source paper "do we need zero training loss after achieving zero training error"):
  with members:
    - "sgd on neural networks learns functions of increasing complexity"
    - "augmenting data with mixup for sentence classification an empirical study"
    - "deep double descent where bigger models and more data hurt"
    - "pumpout a meta approach to robust deep learning with noisy labels"
    - "mixmatch a holistic approach to semi supervised learning"
    - "deep learning for classical japanese literature"
    - "pumpout a meta approach for robustly training deep neural networks with noisy labels"
    - "decoupled weight decay regularization"

Connected through hyperedge HE-4 (induced by source paper "fact check worthiness detection as positive unlabelled learning"):
  with members:
    - "citation needed a taxonomy and algorithmic assessment of wikipedia s verifiability"
    - "distantly supervised named entity recognition using positive unlabeled learning"
    - "claimrank detecting check worthy claims in arabic and english"
    - "automated fact checking task formulations methods and future directions"
    - "learning from positive and unlabeled data a survey"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"

Connected through hyperedge HE-5 (induced by source paper "towards mixture proportion estimation without irreducibility"):
  with members:
    - "dedpul method for mixture proportion estimation and positive unlabeled classification based on density estimation"
    - "are anchor points really indispensable in label noise learning"
    - "dedpul difference of estimated densities based positive unlabeled learning"
    - "mitigating overfitting in supervised classification from two unlabeled datasets a consistent risk correction approach"
    - "masking a new perspective of noisy supervision"
    - "classification from pairwise similarity and unlabeled data"
    - "semi supervised classification based on classification from positive and unlabeled data"
    - "theoretical comparisons of positive unlabeled learning against positive negative learning"

Connected through hyperedge HE-6 (induced by source paper "a variational approach for learning from positive and unlabeled data"):
  with members:
    - "additive margin softmax for face verification"
    - "class prior estimation for learning from positive and unlabeled data"
    - "generative adversarial positive unlabelled learning"
    - "mixture proportion estimation via kernel embedding of distributions"

Connected through hyperedge HE-7 (induced by source paper "positive unlabeled reward learning"):
  with members:
    - "reinforced imitation in heterogeneous action space"
    - "task relevant adversarial imitation learning"
    - "a framework for data driven robotics"
    - "deepmind control suite"
    - "classification from positive unlabeled and biased negative data"
    - "sample efficient imitation learning via generative adversarial nets"
    - "a practical approach to insertion with variable socket position using deep reinforcement learning"
    - "distributed prioritized experience replay"

Connected through hyperedge HE-8 (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"

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)