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: "co sampling training robust networks for extremely noisy supervision"
  Abstract: Training robust deep networks is challenging under noisy labels. Current methodologies focus on estimating the noise transition matrix. However, this matrix is not easy to be estimated exactly. In this paper, free of the matrix estimation, we present a simple but robust learning paradigm called "Co-sampling", which can train deep networks robustly under extremely noisy labels. Briefly, our paradigm trains two networks simultaneously. In each mini-batch data, each network samples its small-loss instances, and cross-trains on such instances from its peer network. We conduct experiments on several simulated noisy datasets. Empirical results demonstrate that, under extremely noisy labels, the Co-sampling approach trains deep learning models robustly.

Connected through hyperedge HE-1 (induced by source paper "nlnl negative learning for noisy labels"):
  with members:
    - "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"
    - "cleannet transfer learning for scalable image classifier training with label noise"

Connected through hyperedge HE-2 (induced by source paper "learning a matching model with co teaching for multi turn response selection in retrieval based dialogue systems"):
  with members:
    - "learning matching models with weak supervision for response selection in retrieval based chatbots"
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "alime assist an intelligent assistant for creating an innovative e commerce experience"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "a sequential matching framework for multi turn response selection in retrieval based chatbots"
    - "fidelity weighted learning"
    - "a persona based neural conversation model"

Connected through hyperedge HE-3 (induced by source paper "label noise robust generative adversarial networks"):
  with members:
    - "cgans with projection discriminator"
    - "video to video synthesis"
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "how good is my gan"
    - "generative adversarial image synthesis with decision tree latent controller"
    - "joint optimization framework for learning with noisy labels"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "noise2noise learning image restoration without clean data"

Connected through hyperedge HE-4 (induced by source paper "limited gradient descent learning with noisy labels"):
  with members:
    - "cognitive information measurements a new perspective"
    - "label less learning for traffic control in an edge network"
    - "deep learning for classical japanese literature"
    - "joint optimization framework for learning with noisy labels"
    - "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"
    - "emnist an extension of mnist to handwritten letters"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-5 (induced by source paper "learning sound events from webly labeled data"):
  with members:
    - "joint optimization framework for learning with noisy labels"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging"
    - "decoupling when to update from how to update"
    - "knowledge transfer from weakly labeled audio using convolutional neural network for sound events and scenes"
    - "understanding deep learning requires rethinking generalization"
    - "cnn architectures for large scale audio classification"
    - "audio event detection using weakly labeled data"

Connected through hyperedge HE-6 (induced by source paper "a two stream mutual attention network for semi supervised biomedical segmentation with noisy labels"):
  with members:
    - "a twofold siamese network for real time object tracking"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "decoupling when to update from how to update"
    - "joint sequence learning and cross modality convolution for 3d biomedical segmentation"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "a deep level set method for image segmentation"
    - "temporal ensembling for semi supervised learning"
    - "u net convolutional networks for biomedical image segmentation"

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)