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: "loss factorization weakly supervised learning and label noise robustness"
  Abstract: We prove that the empirical risk of most well-known loss functions factors into a linear term aggregating all labels with a term that is label free, and can further be expressed by sums of the loss. This holds true even for non-smooth, non-convex losses and in any RKHS. The first term is a (kernel) mean operator --the focal quantity of this work-- which we characterize as the sufficient statistic for the labels. The result tightens known generalization bounds and sheds new light on their interpretation. #R##N#Factorization has a direct application on weakly supervised learning. In particular, we demonstrate that algorithms like SGD and proximal methods can be adapted with minimal effort to handle weak supervision, once the mean operator has been estimated. We apply this idea to learning with asymmetric noisy labels, connecting and extending prior work. Furthermore, we show that most losses enjoy a data-dependent (by the mean operator) form of noise robustness, in contrast with known negative results.

Connected through hyperedge HE-1 (induced by source paper "does label smoothing mitigate label noise"):
  with members:
    - "regularization via structural label smoothing"
    - "are anchor points really indispensable in label noise learning"
    - "regularized evolution for image classifier architecture search"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "masking a new perspective of noisy supervision"
    - "pumpout a meta approach for robustly training deep neural networks with noisy labels"
    - "on calibration of modern neural networks"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-2 (induced by source paper "positive unlabeled reward learning"):
  with members:
    - "reinforced imitation in heterogeneous action space"
    - "task relevant adversarial imitation learning"
    - "a framework for data driven robotics"
    - "deepmind control suite"
    - "classification from positive unlabeled and biased negative data"
    - "sample efficient imitation learning via generative adversarial nets"
    - "a practical approach to insertion with variable socket position using deep reinforcement learning"
    - "distributed prioritized experience replay"

Connected through hyperedge HE-3 (induced by source paper "confidence scores make instance dependent label noise learning possible"):
  with members:
    - "learning with bad training data via iterative trimmed loss minimization"
    - "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"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "mixup beyond empirical risk minimization"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-4 (induced by source paper "semi supervised ordinal regression based on empirical risk minimization"):
  with members:
    - "chainer a deep learning framework for accelerating the research cycle"
    - "classification from pairwise similarity and unlabeled data"
    - "positive unlabeled learning with non negative risk estimator"
    - "semi supervised classification based on classification from positive and unlabeled data"
    - "theoretical comparisons of positive unlabeled learning against positive negative learning"
    - "on the consistency of ordinal regression methods"
    - "transductive ordinal regression"
    - "agnostic learning of monomials by halfspaces is hard"

Connected through hyperedge HE-5 (induced by source paper "learning with inadequate and incorrect supervision"):
  with members:
    - "learning deep networks from noisy labels with dropout regularization"
    - "on the convergence of a family of robust losses for stochastic gradient descent"
    - "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-6 (induced by source paper "cost sensitive learning in the presence of symmetric label noise"):
  with members:
    - "learning with symmetric label noise the importance of being unhinged"
    - "making risk minimization tolerant to label noise"
    - "noise tolerance under risk minimization"

Connected through hyperedge HE-7 (induced by source paper "learning graph neural networks with noisy labels"):
  with members:
    - "how powerful are graph neural networks"
    - "poisoning attacks against support vector machines"

Connected through hyperedge HE-8 (induced by source paper "a semi supervised two stage approach to learning from noisy labels"):
  with members:
    - "deep learning is robust to massive label noise"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "webvision database visual learning and understanding from web data"
    - "toward robustness against label noise in training deep discriminative neural networks"
    - "learning from noisy large scale datasets with minimal supervision"
    - "learning from noisy labels with distillation"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "attend in groups a weakly supervised deep learning framework for learning from web data"

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)