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 classification based on classification from positive and unlabeled data"
  Abstract: Most of the semi-supervised classification methods developed so far use unlabeled data for regularization purposes under particular distributional assumptions such as the cluster assumption. In contrast, recently developed methods of classification from positive and unlabeled data (PU classification) use unlabeled data for risk evaluation, i.e., label information is directly extracted from unlabeled data. In this paper, we extend PU classification to also incorporate negative data and propose a novel semi-supervised classification approach. We establish generalization error bounds for our novel methods and show that the bounds decrease with respect to the number of unlabeled data without the distributional assumptions that are required in existing semi-supervised classification methods. Through experiments, we demonstrate the usefulness of the proposed methods.

Connected through hyperedge HE-1 (induced by source paper "combating noisy labels by agreement a joint training method with co regularization"):
  with members:
    - "partial label learning with self guided retraining"
    - "learning with inadequate and incorrect supervision"
    - "are anchor points really indispensable in label noise learning"
    - "nlnl negative learning for noisy labels"
    - "how does disagreement help generalization against label corruption"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "joint optimization framework for learning with noisy labels"
    - "learning to reweight examples for robust deep learning"

Connected through hyperedge HE-2 (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"
    - "positive unlabeled learning with non negative risk estimator"
    - "theoretical comparisons of positive unlabeled learning against positive negative learning"

Connected through hyperedge HE-3 (induced by source paper "binary matrix completion with nonconvex regularizers"):
  with members:
    - "link prediction via matrix completion"
    - "positive unlabeled learning with non negative risk estimator"
    - "large scale low rank matrix learning with nonconvex regularizers"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "matrix completion via max norm constrained optimization"
    - "network topology inference from spectral templates"
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "social trust prediction via max norm constrained 1 bit matrix completion"

Connected through hyperedge HE-4 (induced by source paper "scalable evaluation and improvement of document set expansion via neural positive unlabeled learning"):
  with members:
    - "scibert pretrained contextualized embeddings for scientific text"
    - "revisiting sample selection approach to positive unlabeled learning turning unlabeled data into positive rather than negative"
    - "classification from positive unlabeled and biased negative data"
    - "alternate estimation of a classifier and the class prior from positive and unlabeled data"
    - "imbalanced deep learning by minority class incremental rectification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep imbalanced learning for face recognition and attribute prediction"
    - "attention is all you need"

Connected through hyperedge HE-5 (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"
    - "generative adversarial positive unlabelled learning"
    - "theoretical comparisons of positive unlabeled learning against positive negative learning"
    - "mixture proportion estimation via kernel embedding of distributions"

Connected through hyperedge HE-6 (induced by source paper "semi supervised ordinal regression based on empirical risk minimization"):
  with members:
    - "chainer a deep learning framework for accelerating the research cycle"
    - "classification from pairwise similarity and unlabeled data"
    - "positive unlabeled learning with non negative risk estimator"
    - "loss factorization weakly supervised learning and label noise robustness"
    - "theoretical comparisons of positive unlabeled learning against positive negative learning"
    - "on the consistency of ordinal regression methods"
    - "transductive ordinal regression"
    - "agnostic learning of monomials by halfspaces is hard"

Connected through hyperedge HE-7 (induced by source paper "efficient training for positive unlabeled learning"):
  with members:
    - "theoretical comparisons of learning from positive negative positive unlabeled and negative unlabeled data"
    - "one class classification taxonomy of study and review of techniques"
    - "pu learning for matrix completion"
    - "semi supervised learning with deep generative models"
    - "convex and scalable weakly labeled svms"

Connected through hyperedge HE-8 (induced by source paper "uncoupled regression from pairwise comparison data"):
  with members:
    - "classification from pairwise similarity and unlabeled data"
    - "linear regression without correspondence"
    - "theoretical comparisons of positive unlabeled learning against positive negative learning"

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)