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: "on the convergence of a family of robust losses for stochastic gradient descent"
  Abstract: The convergence of Stochastic Gradient Descent (SGD) using convex loss functions has been widely studied. However, vanilla SGD methods using convex losses cannot perform well with noisy labels, which adversely affect the update of the primal variable in SGD methods. Unfortunately, noisy labels are ubiquitous in real world applications such as crowdsourcing. To handle noisy labels, in this paper, we present a family of robust losses for SGD methods. By employing our robust losses, SGD methods successfully reduce negative effects caused by noisy labels on each update of the primal variable. We not only reveal that the convergence rate is O(1/T) for SGD methods using robust losses, but also provide the robustness analysis on two representative robust losses. Comprehensive experimental results on six real-world datasets show that SGD methods using robust losses are obviously more robust than other baseline methods in most situations with fast convergence.

Connected through hyperedge HE-1 (induced by source paper "learning with inadequate and incorrect supervision"):
  with members:
    - "learning deep networks from noisy labels with dropout regularization"
    - "loss factorization weakly supervised learning and label noise robustness"
    - "learning with symmetric label noise the importance of being unhinged"
    - "making risk minimization tolerant to label noise"
    - "robust recovery of subspace structures by low rank representation"
    - "identifying mislabeled training data"

Connected through hyperedge HE-2 (induced by source paper "masking a new perspective of noisy supervision"):
  with members:
    - "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"
    - "iterative learning with open set noisy labels"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-3 (induced by source paper "co sampling training robust networks for extremely noisy supervision"):
  with members:
    - "joint optimization framework for learning with noisy labels"
    - "dimensionality driven learning with noisy labels"
    - "learning to reweight examples for robust deep learning"
    - "iterative learning with open set noisy labels"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "decoupling when to update from how to update"
    - "learning from noisy large scale datasets with minimal supervision"
    - "learning from noisy labels with distillation"

Connected through hyperedge HE-4 (induced by source paper "mitigating sybils in federated learning poisoning"):
  with members:
    - "analyzing federated learning through an adversarial lens"
    - "how to backdoor federated learning"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "differentially private federated learning a client level perspective"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "targeted backdoor attacks on deep learning systems using data poisoning"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-5 (induced by source paper "millionaire a hint guided approach for crowdsourcing"):
  with members:
    - "end to end learning for self driving cars"
    - "crowdsourcing with unsure option"
    - "a neural network approach to context sensitive generation of conversational responses"
    - "incentivizing high quality crowdwork"
    - "training convolutional networks with noisy labels"
    - "imagenet large scale visual recognition challenge"
    - "double or nothing multiplicative incentive mechanisms for crowdsourcing"
    - "evaluating the crowd with confidence"

Connected through hyperedge HE-6 (induced by source paper "privacy preserving stochastic gradual learning"):
  with members:
    - "webly supervised learning of convolutional networks"
    - "calibrating data to sensitivity in private data analysis"
    - "better mini batch algorithms via accelerated gradient methods"

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)