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: "what objective does self paced learning indeed optimize"
  Abstract: Self-paced learning (SPL) is a recently raised methodology designed through simulating the learning principle of humans/animals. A variety of SPL realization schemes have been designed for different computer vision and pattern recognition tasks, and empirically substantiated to be effective in these applications. However, the investigation on its theoretical insight is still a blank. To this issue, this study attempts to provide some new theoretical understanding under the SPL scheme. Specifically, we prove that the solving strategy on SPL accords with a majorization minimization algorithm implemented on a latent objective function. Furthermore, we find that the loss function contained in this latent objective has a similar configuration with non-convex regularized penalty (NSPR) known in statistics and machine learning. Such connection inspires us discovering more intrinsic relationship between SPL regimes and NSPR forms, like SCAD, LOG and EXP. The robustness insight under SPL can then be finely explained. We also analyze the capability of SPL on its easy loss prior embedding property, and provide an insightful interpretation to the effectiveness mechanism under previous SPL variations. Besides, we design a group-partial-order loss prior, which is especially useful to weakly labeled large-scale data processing tasks. Through applying SPL with this loss prior to the FCVID dataset, which is currently one of the biggest manually annotated video dataset, our method achieves state-of-the-art performance beyond previous methods, which further helps supports the proposed theoretical arguments.

Connected through hyperedge HE-1 (induced by source paper "derivative manipulation for general example weighting"):
  with members:
    - "symmetric cross entropy for robust learning with noisy labels"
    - "nlnl negative learning for noisy labels"
    - "deep learning is robust to massive label noise"
    - "joint optimization framework for learning with noisy labels"
    - "dimensionality driven learning with noisy labels"
    - "deep metric learning by online soft mining and class aware attention"
    - "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 "derivative manipulation for adjusting emphasis density function a general example weighting framework"):
  with members:
    - "symmetric cross entropy for robust learning with noisy labels"
    - "nlnl negative learning for noisy labels"
    - "improving mae against cce under label noise"
    - "deep learning is robust to massive label noise"
    - "joint optimization framework for learning with noisy labels"
    - "dimensionality driven learning with noisy labels"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "deep metric learning by online soft mining and class aware attention"

Connected through hyperedge HE-3 (induced by source paper "bi skip a motion deblurring network using self paced learning"):
  with members:
    - "scale recurrent network for deep image deblurring"
    - "deep image prior"
    - "learning blind motion deblurring"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "improved training of wasserstein gans"
    - "perceptual losses for real time style transfer and super resolution"
    - "learning fully convolutional networks for iterative non blind deconvolution"
    - "improved techniques for training gans"

Connected through hyperedge HE-4 (induced by source paper "self paced probabilistic principal component analysis for data with outliers"):
  with members:
    - "self paced deep learning for weakly supervised object detection"
    - "online robust principal component analysis with change point detection"
    - "efficient l1 norm principal component analysis via bit flipping"
    - "bridging saliency detection to weakly supervised object detection based on self paced curriculum learning"
    - "robust principal component analysis"

Connected through hyperedge HE-5 (induced by source paper "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "focal loss for dense object detection"
    - "tag prediction at flickr a view from the darkroom"
    - "understanding deep learning requires rethinking generalization"
    - "youtube 8m a large scale video classification benchmark"
    - "wide residual networks"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-6 (induced by source paper "boosted zero shot learning with semantic correlation regularization"):
  with members:
    - "zero shot learning through cross modal transfer"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "a self paced regularization framework for partial label learning"):
  with members:
    - "a regularization approach for instance based superset label learning"
    - "self paced deep learning for weakly supervised object detection"
    - "learning from ambiguously labeled face images"
    - "solving multiclass learning problems via error correcting output codes"

Connected through hyperedge HE-8 (induced by source paper "self paced learning an implicit regularization perspective"):
  with members:
    - "a survey on multi view learning"
    - "approximate computation and implicit regularization for very large scale data analysis"
    - "implementing regularization implicitly via approximate eigenvector computation"

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)