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: "deep bilevel learning"
  Abstract: We present a novel regularization approach to train neural networks that enjoys better generalization and test error than standard stochastic gradient descent. Our approach is based on the principles of cross-validation, where a validation set is used to limit the model overfitting. We formulate such principles as a bilevel optimization problem. This formulation allows us to define the optimization of a cost on the validation set subject to another optimization on the training set. The overfitting is controlled by introducing weights on each mini-batch in the training set and by choosing their values so that they minimize the error on the validation set. In practice, these weights define mini-batch learning rates in a gradient descent update equation that favor gradients with better generalization capabilities. Because of its simplicity, this approach can be integrated with other regularization methods and training schemes. We evaluate extensively our proposed algorithm on several neural network architectures and datasets, and find that it consistently improves the generalization of the model, especially when labels are noisy.

Connected through hyperedge HE-1 (induced by source paper "addressing the loss metric mismatch with adaptive loss alignment"):
  with members:
    - "autoloss learning discrete schedules for alternate optimization"
    - "learning to teach with dynamic loss functions"
    - "cosface large margin cosine loss for deep face recognition"
    - "snas stochastic neural architecture search"
    - "learning to reweight examples for robust deep learning"
    - "deep metric learning with hierarchical triplet loss"
    - "attention based ensemble for deep metric learning"
    - "sampling matters in deep embedding learning"

Connected through hyperedge HE-2 (induced by source paper "self learning to filter noisy labels with self ensembling"):
  with members:
    - "symmetric cross entropy for robust learning with noisy labels"
    - "deep learning is robust to massive label noise"
    - "anomaly detection with multiple hypotheses predictions"
    - "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"
    - "learning to reweight examples for robust deep learning"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"

Connected through hyperedge HE-3 (induced by source paper "uncertainty based detection and relabeling of noisy image labels"):
  with members:
    - "unsupervised label noise modeling and loss correction"
    - "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"
    - "cleannet transfer learning for scalable image classifier training with label noise"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"

Connected through hyperedge HE-4 (induced by source paper "robust learning under label noise with iterative noise filtering"):
  with members:
    - "learning to reweight examples for robust deep learning"
    - "iterative learning with open set noisy labels"
    - "good semi supervised learning that requires a bad gan"
    - "semi supervised learning with gans manifold invariance with improved inference"
    - "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"
    - "shake shake regularization"
    - "accurate large minibatch sgd training imagenet in 1 hour"

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.CV(Computer Vision and Pattern Recognition)