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: "nlnl negative learning for noisy labels"
  Abstract: Convolutional Neural Networks (CNNs) provide excellent performance when used for image classification. The classical method of training CNNs is by labeling images in a supervised manner as in "input image belongs to this label" (Positive Learning; PL), which is a fast and accurate method if the labels are assigned correctly to all images. However, if inaccurate labels, or noisy labels, exist, training with PL will provide wrong information, thus severely degrading performance. To address this issue, we start with an indirect learning method called Negative Learning (NL), in which the CNNs are trained using a complementary label as in "input image does not belong to this complementary label." Because the chances of selecting a true label as a complementary label are low, NL decreases the risk of providing incorrect information. Furthermore, to improve convergence, we extend our method by adopting PL selectively, termed as Selective Negative Learning and Positive Learning (SelNLPL). PL is used selectively to train upon expected-to-be-clean data, whose choices become possible as NL progresses, thus resulting in superior performance of filtering out noisy data. With simple semi-supervised training technique, our method achieves state-of-the-art accuracy for noisy data classification, proving the superiority of SelNLPL's noisy data filtering ability.

Connected through hyperedge HE-1 (induced by source paper "learning from web data with self organizing memory module"):
  with members:
    - "symmetric cross entropy for robust learning with noisy labels"
    - "c mil continuation multiple instance learning for weakly supervised object detection"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "learning from web data the benefit of unsupervised object localization"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "attention based deep multiple instance learning"
    - "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 "derivative manipulation for general example weighting"):
  with members:
    - "symmetric cross entropy for robust learning with 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"
    - "masking a new perspective of noisy supervision"

Connected through hyperedge HE-3 (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"
    - "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-4 (induced by source paper "no regret sample selection with noisy labels"):
  with members:
    - "introduction to online convex optimization"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "joint optimization framework for learning with noisy labels"
    - "not all samples are created equal deep learning with importance sampling"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "a semi supervised two stage approach to learning from noisy labels"
    - "learning from noisy large scale datasets with minimal supervision"
    - "learning from noisy labels with distillation"

Connected through hyperedge HE-5 (induced by source paper "bridging ordinary label learning and complementary label learning"):
  with members:
    - "learning from multiple complementary labels"
    - "densely connected convolutional networks"
    - "semi supervised classification with graph convolutional networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "towards robust learning with different label noise distributions"):
  with members:
    - "deep self learning from noisy labels"
    - "are anchor points really indispensable in label noise learning"
    - "accelerating deep learning by focusing on the biggest losers"
    - "pseudo labeling and confirmation bias in deep semi supervised learning"
    - "unsupervised label noise modeling and loss correction"
    - "using pre training can improve model robustness and uncertainty"
    - "mixmatch a holistic approach to semi supervised learning"
    - "understanding and utilizing deep neural networks trained with noisy labels"

Connected through hyperedge HE-7 (induced by source paper "learning from synthetic animals"):
  with members:
    - "three d safari learning to estimate zebra pose shape and texture from images in the wild"
    - "confidence regularized self training"
    - "multi adversarial faster rcnn for unrestricted object detection"
    - "a robust learning approach to domain adaptive object detection"
    - "automatic adaptation of object detectors to new domains using self training"
    - "bidirectional learning for domain adaptation of semantic segmentation"
    - "diversify and match a domain adaptive representation learning paradigm for object detection"
    - "amur tiger re identification in the wild"

Connected through hyperedge HE-8 (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"
    - "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"
    - "learning to reweight examples for robust deep 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)