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: "making risk minimization tolerant to label noise"
  Abstract: In many applications, the training data, from which one needs to learn a classifier, is corrupted with label noise. Many standard algorithms such as SVM perform poorly in the presence of label noise. In this paper we investigate the robustness of risk minimization to label noise. We prove a sufficient condition on a loss function for the risk minimization under that loss to be tolerant to uniform label noise. We show that the 0-1 loss, sigmoid loss, ramp loss and probit loss satisfy this condition though none of the standard convex loss functions satisfy it. We also prove that, by choosing a sufficiently large value of a parameter in the loss function, the sigmoid loss, ramp loss and probit loss can be made tolerant to non-uniform label noise also if we can assume the classes to be separable under noise-free data distribution. Through extensive empirical studies, we show that risk minimization under the 0-1 loss, the sigmoid loss and the ramp loss has much better robustness to label noise when compared to the SVM algorithm.

Connected through hyperedge HE-1 (induced by source paper "learning adaptive loss for robust learning with noisy labels"):
  with members:
    - "symmetric cross entropy for robust learning with noisy labels"
    - "curriculum loss robust learning and generalization against label corruption"
    - "addressing the loss metric mismatch with adaptive loss alignment"
    - "l_dmi an information theoretic noise robust loss function"
    - "learning surrogate losses"
    - "meta weight net learning an explicit mapping for sample weighting"
    - "robust bi tempered logistic loss based on bregman divergences"
    - "joint optimization framework for learning with noisy labels"

Connected through hyperedge HE-2 (induced by source paper "a non intrusive correction algorithm for classification problems with corrupted data"):
  with members:
    - "stable architectures for deep neural networks"
    - "learning to reweight examples for robust deep learning"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "certified defenses for data poisoning attacks"
    - "learning from noisy singly labeled data"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "learning from noisy large scale datasets with minimal supervision"

Connected through hyperedge HE-3 (induced by source paper "robust binary classification with the 01 loss"):
  with members:
    - "hopskipjumpattack a query efficient decision based attack"
    - "curriculum loss robust learning and generalization against label corruption"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training can hurt generalization"
    - "darts deceiving autonomous cars with toxic signs"
    - "attacking binarized neural networks"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"

Connected through hyperedge HE-4 (induced by source paper "no regret sample selection with noisy labels"):
  with members:
    - "introduction to online convex optimization"
    - "nlnl negative learning for noisy labels"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "deep self learning from noisy labels"):
  with members:
    - "improving referring expression grounding with cross modal attention guided erasing"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "joint optimization framework for learning with noisy labels"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "learning to reweight examples for robust deep learning"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "using trusted data to train deep networks on labels corrupted by severe noise"

Connected through hyperedge HE-6 (induced by source paper "nlnl negative learning for noisy labels"):
  with members:
    - "co sampling training robust networks for extremely noisy supervision"
    - "joint optimization framework for learning with noisy labels"
    - "dimensionality driven learning with noisy labels"
    - "learning to reweight examples for robust deep learning"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "iterative learning with open set noisy labels"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "a semi supervised two stage approach to learning from noisy labels"

Connected through hyperedge HE-7 (induced by source paper "confidence scores make instance dependent label noise learning possible"):
  with members:
    - "learning with bad training data via iterative trimmed loss minimization"
    - "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"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "mixup beyond empirical risk minimization"
    - "loss factorization weakly supervised learning and label noise robustness"

Connected through hyperedge HE-8 (induced by source paper "mitigating overfitting in supervised classification from two unlabeled datasets a consistent risk correction approach"):
  with members:
    - "deep learning for classical japanese literature"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "positive unlabeled learning with non negative risk estimator"
    - "size independent sample complexity of neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "smooth neighbors on teacher graphs for semi supervised learning"
    - "theoretical comparisons of positive unlabeled learning against positive negative learning"
    - "temporal ensembling for semi supervised 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)