human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "stochastic generalized adversarial label learning"):
  Abstract: The usage of machine learning models has grown substantially and is spreading into several application domains. A common need in using machine learning models is collecting the data required to train these models. In some cases, labeling a massive dataset can be a crippling bottleneck, so there is need to develop models that work when training labels for large amounts of data are not easily obtained. A possible solution is weak supervision, which uses noisy labels that are easily obtained from multiple sources. The challenge is how best to combine these noisy labels and train a model to perform well given a task. In this paper, we propose stochastic generalized adversarial label learning (Stoch-GALL), a framework for training machine learning models that perform well when noisy and possibly correlated labels are provided. Our framework allows users to provide different weak labels and multiple constraints on these labels. Our model then attempts to learn parameters for the data by solving a non-zero sum game optimization. The game is between an adversary that chooses labels for the data and a model that minimizes the error made by the adversarial labels. We test our method on three datasets by training convolutional neural network models that learn to classify image objects with limited access to training labels. Our approach is able to learn even in settings where the weak supervision confounds state-of-the-art weakly supervised learning methods. The results of our experiments demonstrate the applicability of this approach to general classification tasks.

Includes members:
  Member "snorkel drybell a case study in deploying weak supervision at industrial scale":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "slice tuner a selective data collection framework for accurate and fair machine learning models")
  Member "adversarial label learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "active learning by greedy split and label exploration")
  Member "unsupervised risk estimation using only conditional independence structure":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an adaptable framework for deep adversarial label learning from weak supervision")
  Member "unsupervised ensemble learning with dependent classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "agreement rate initialized maximum likelihood estimator for ensemble classifier aggregation and its application in brain computer interface")
  Member "optimally combining classifiers using unlabeled data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reliable weakly supervised learning maximize gain and maintain safeness")
  Member "flickr30k entities collecting region to phrase correspondences for richer image to sentence models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cops ref a new dataset and task on compositional referring expression comprehension")
  Member "scalable semi supervised aggregation of classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "inferring generative model structure with static analysis")

Question: Please tell me which class the source paper 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)