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: "noise tolerance under risk minimization"
  Abstract: In this paper, we explore noise-tolerant learning of classifiers. We formulate the problem as follows. We assume that there is an unobservable training set that is noise free. The actual training set given to the learning algorithm is obtained from this ideal data set by corrupting the class label of each example. The probability that the class label of an example is corrupted is a function of the feature vector of the example. This would account for most kinds of noisy data one encounters in practice. We say that a learning method is noise tolerant if the classifiers learnt with noise-free data and with noisy data, both have the same classification accuracy on the noise-free data. In this paper, we analyze the noise-tolerance properties of risk minimization (under different loss functions). We show that risk minimization under 0-1 loss function has impressive noise-tolerance properties and that under squared error loss is tolerant only to uniform noise; risk minimization under other loss functions is not noise tolerant. We conclude this paper with some discussion on the implications of these theoretical results.

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 "learning a directional soft lane affordance model for road scenes using self supervision"):
  with members:
    - "self driving cars a survey"
    - "learning human aware path planning with fully convolutional networks"
    - "graph r cnn for scene graph generation"
    - "learning long range perception using self supervision from short range sensors and odometry"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "short term motion prediction of traffic actors for autonomous driving using deep convolutional networks"
    - "a parametric top view representation of complex road scenes"
    - "pixels to graphs by associative embedding"

Connected through hyperedge HE-3 (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-4 (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-5 (induced by source paper "semi supervised cross modal retrieval with label prediction"):
  with members:
    - "learning from noisy large scale datasets with minimal supervision"
    - "deep cross modal hashing"
    - "deep residual learning for image recognition"
    - "learning visual features from large weakly supervised data"
    - "caffe convolutional architecture for fast feature embedding"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "l_dmi an information theoretic noise robust loss function"):
  with members:
    - "max mig an information theoretic approach for joint learning from crowds"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "joint optimization framework for learning with noisy labels"
    - "water from two rocks maximizing the mutual information"
    - "dimensionality driven learning with noisy labels"
    - "learning to reweight examples for robust deep learning"
    - "masking a new perspective of noisy supervision"
    - "using trusted data to train deep networks on labels corrupted by severe noise"

Connected through hyperedge HE-7 (induced by source paper "an effective label noise model for dnn text classification"):
  with members:
    - "deep learning is robust to massive label noise"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "masking a new perspective of noisy supervision"
    - "a surprising linear relationship predicts test performance in deep networks"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "recent trends in deep learning based natural language processing"
    - "learning deep networks from noisy labels with dropout regularization"
    - "deep learning from noisy image labels with quality embedding"

Connected through hyperedge HE-8 (induced by source paper "product image recognition with guidance learning and noisy supervision"):
  with members:
    - "rpc a large scale retail product checkout dataset"
    - "deep learning is robust to massive label noise"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "joint optimization framework for learning with noisy labels"
    - "cleannet transfer learning for scalable image classifier training with label noise"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "webvision database visual learning and understanding from web data"
    - "learning from noisy large scale datasets with minimal supervision"

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)