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: "optimally combining classifiers using unlabeled data"
  Abstract: We develop a worst-case analysis of aggregation of classifier ensembles for binary classification. The task of predicting to minimize error is formulated as a game played over a given set of unlabeled data (a transductive setting), where prior label information is encoded as constraints on the game. The minimax solution of this game identifies cases where a weighted combination of the classifiers can perform significantly better than any single classifier.

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"
    - "adversarial label learning"
    - "unsupervised risk estimation using only conditional independence structure"
    - "unsupervised ensemble learning with dependent classifiers"
    - "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 "reliable weakly supervised learning maximize gain and maintain safeness"):
  with members:
    - "training set debugging using trusted items"
    - "learning to reweight examples for robust deep learning"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "training deep neural networks on noisy labels with bootstrapping"
    - "learning from labeled and unlabeled data an empirical study across techniques and domains"

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

Connected through hyperedge HE-4 (induced by source paper "scalable semi supervised aggregation of classifiers"):
  with members:
    - "imagenet large scale visual recognition challenge"
    - "feature hashing for large scale multitask learning"

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)