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: "classification from pairwise similarity and unlabeled data"
  Abstract: One of the biggest bottlenecks in supervised learning is its high labeling cost. To overcome this problem, we propose a new weakly-supervised learning setting called SU classification, where only similar (S) data pairs (two examples belong to the same class) and unlabeled (U) data are needed, instead of fully-supervised data. We show that an unbiased estimator of the classification risk can be obtained only from SU data, and its empirical risk minimizer achieves the optimal parametric convergence rate. Finally, we demonstrate the effectiveness of the proposed method through experiments.

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"
    - "positive unlabeled learning with non negative risk estimator"
    - "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-3 (induced by source paper "multi class classification without multi class labels"):
  with members:
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "visualizing the loss landscape of neural nets"
    - "learning to cluster in order to transfer across domains and tasks"
    - "semi supervised kernel metric learning using relative comparisons"
    - "identity mappings in deep residual networks"
    - "matching networks for one shot learning"
    - "an empirical analysis of the optimization of deep network loss surfaces"

Connected through hyperedge HE-4 (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"
    - "positive unlabeled learning with non negative risk estimator"
    - "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"
    - "on the consistency of ordinal regression methods"
    - "transductive ordinal regression"
    - "agnostic learning of monomials by halfspaces is hard"

Connected through hyperedge HE-5 (induced by source paper "learning from multiple complementary labels"):
  with members:
    - "partial label learning with self guided retraining"
    - "generative discriminative complementary learning"
    - "a regularization approach for instance based superset label learning"
    - "online multiclass classification based on prediction margin for partial feedback"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "uncoupled regression from pairwise comparison data"):
  with members:
    - "linear regression without correspondence"
    - "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-7 (induced by source paper "classification from pairwise similarities dissimilarities and unlabeled data via empirical risk minimization"):
  with members:
    - "semi supervised classification based on classification from positive and unlabeled data"
    - "information theoretic semi supervised metric learning via entropy regularization"

Connected through hyperedge HE-8 (induced by source paper "classification from triplet comparison data"):
  with members:
    - "classification from pairwise similarities dissimilarities and unlabeled data via empirical risk minimization"
    - "information theoretic semi supervised metric learning via entropy regularization"

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)