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: "partial label learning with self guided retraining"
  Abstract: Partial label learning deals with the problem where each training instance is assigned a set of candidate labels, only one of which is correct. This paper provides the first attempt to leverage the idea of self-training for dealing with partially labeled examples. Specifically, we propose a unified formulation with proper constraints to train the desired model and perform pseudo-labeling jointly. For pseudo-labeling, unlike traditional self-training that manually differentiates the ground-truth label with enough high confidence, we introduce the maximum infinity norm regularization on the modeling outputs to automatically achieve this consideratum, which results in a convex-concave optimization problem. We show that optimizing this convex-concave problem is equivalent to solving a set of quadratic programming (QP) problems. By proposing an upper-bound surrogate objective function, we turn to solving only one QP problem for improving the optimization efficiency. Extensive experiments on synthesized and real-world datasets demonstrate that the proposed approach significantly outperforms the state-of-the-art partial label learning approaches.

Connected through hyperedge HE-1 (induced by source paper "combating noisy labels by agreement a joint training method with co regularization"):
  with members:
    - "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"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"

Connected through hyperedge HE-2 (induced by source paper "general partial label learning via dual bipartite graph autoencoder"):
  with members:
    - "m vad names a dataset for video captioning with naming"
    - "a regularization approach for instance based superset label learning"
    - "link prediction based on graph neural networks"
    - "generating descriptions with grounded and co referenced people"
    - "predicting multicellular function through multi layer tissue networks"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-3 (induced by source paper "network cooperation with progressive disambiguation for partial label learning"):
  with members:
    - "learning with inadequate and incorrect supervision"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "learning from ambiguously labeled face images"
    - "understanding deep learning requires rethinking generalization"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "progressive identification of true labels for partial label learning"):
  with members:
    - "learning from ambiguously labeled face images"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "hera partial label learning by combining heterogeneous loss with sparse and low rank regularization"):
  with members:
    - "a regularization approach for instance based superset label learning"
    - "learning from ambiguously labeled face images"
    - "robust recovery of subspace structures by low rank representation"
    - "large scale multi label learning with missing labels"
    - "solving multiclass learning problems via error correcting output codes"

Connected through hyperedge HE-6 (induced by source paper "learning from multiple complementary labels"):
  with members:
    - "generative discriminative complementary learning"
    - "a regularization approach for instance based superset label learning"
    - "online multiclass classification based on prediction margin for partial feedback"
    - "classification from pairwise similarity and unlabeled data"
    - "adam a method for stochastic optimization"

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)