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: "adversarial label learning"
  Abstract: We consider the task of training classifiers without labels. We propose a weakly supervised method---adversarial label learning---that trains classifiers to perform well against an adversary that chooses labels for training data. The weak supervision constrains what labels the adversary can choose. The method therefore minimizes an upper bound of the classifier's error rate using projected primal-dual subgradient descent. Minimizing this bound protects against bias and dependencies in the weak supervision. Experiments on three real datasets show that our method can train without labels and outperforms other approaches for weakly supervised learning.

Connected through hyperedge HE-1 (induced by source paper "stochastic generalized adversarial label learning"):
  with members:
    - "snorkel drybell a case study in deploying weak supervision at industrial scale"
    - "unsupervised risk estimation using only conditional independence structure"
    - "unsupervised ensemble learning with dependent classifiers"
    - "optimally combining classifiers using unlabeled data"
    - "flickr30k entities collecting region to phrase correspondences for richer image to sentence models"
    - "scalable semi supervised aggregation of classifiers"

Connected through hyperedge HE-2 (induced by source paper "active learning by greedy split and label exploration"):
  with members:
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "importance weighted active learning"
    - "active learning with statistical models"

Connected through hyperedge HE-3 (induced by source paper "an adaptable framework for deep adversarial label learning from weak supervision"):
  with members:
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "unsupervised risk estimation using only conditional independence structure"
    - "optimally combining classifiers using unlabeled 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)