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: "l_dmi an information theoretic noise robust loss function"
  Abstract: Accurately annotating large scale dataset is notoriously expensive both in time and in money. Although acquiring low-quality-annotated dataset can be much cheaper, it often badly damages the performance of trained models when using such dataset without particular treatment. Various methods have been proposed for learning with noisy labels. However, most methods only handle limited kinds of noise patterns, require auxiliary information or steps (e.g. , knowing or estimating the noise transition matrix), or lack theoretical justification. In this paper, we propose a novel information-theoretic loss function, $\mathcal{L}_{DMI}$, for training deep neural networks robust to label noise. The core of $\mathcal{L}_{DMI}$ is a generalized version of mutual information, termed Determinant based Mutual Information (DMI), which is not only information-monotone but also relatively invariant. \emph{To the best of our knowledge, $\mathcal{L}_{DMI}$ is the first loss function that is provably robust to instance-independent label noise, regardless of noise pattern, and it can be applied to any existing classification neural networks straightforwardly without any auxiliary information}. In addition to theoretical justification, we also empirically show that using $\mathcal{L}_{DMI}$ outperforms all other counterparts in the classification task on both image dataset and natural language dataset include Fashion-MNIST, CIFAR-10, Dogs vs. Cats, MR with a variety of synthesized noise patterns and noise amounts, as well as a real-world dataset Clothing1M. Codes are available at this https URL .

Connected through hyperedge HE-1 (induced by source paper "identifying mislabeled data using the area under the margin ranking"):
  with members:
    - "dividemix learning with noisy labels as semi supervised learning"
    - "convolutional networks with dense connectivity"
    - "simple and effective regularization methods for training on noisily labeled data with generalization guarantee"
    - "towards robust learning with different label noise distributions"
    - "self learning to filter noisy labels with self ensembling"
    - "image classification with deep learning in the presence of noisy labels a survey"
    - "unsupervised label noise modeling and loss correction"
    - "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"

Connected through hyperedge HE-2 (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"
    - "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"
    - "learning to reweight examples for robust deep learning"

Connected through hyperedge HE-3 (induced by source paper "learning not to learn in the presence of noisy labels"):
  with members:
    - "laprop a better way to combine momentum with adaptive gradient"
    - "deep gamblers learning to abstain with portfolio theory"
    - "sgd on neural networks learns functions of increasing complexity"
    - "simple and effective regularization methods for training on noisily labeled data with generalization guarantee"
    - "how does disagreement help generalization against label corruption"
    - "selectivenet a deep neural network with an integrated reject option"
    - "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"
    - "gradient descent finds global minima of deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "peer loss functions learning from noisy labels without knowing noise rates"):
  with members:
    - "water from two rocks maximizing the mutual information"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "informed truthfulness in multi task peer prediction"
    - "an information theoretic framework for designing information elicitation mechanisms that reward truth telling"
    - "learning with symmetric label noise the importance of being unhinged"
    - "deep residual learning for image recognition"
    - "making risk minimization tolerant to label noise"
    - "training convolutional networks with noisy labels"

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)