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: "analysis of semi supervised learning with the yarowsky algorithm"
  Abstract: The Yarowsky algorithm is a rule-based semi-supervised learning algorithm that has been successfully applied to some problems in computational linguistics. The algorithm was not mathematically well understood until (Abney 2004) which analyzed some specific variants of the algorithm, and also proposed some new algorithms for bootstrapping. In this paper, we extend Abney's work and show that some of his proposed algorithms actually optimize (an upper-bound on) an objective function based on a new definition of cross-entropy which is based on a particular instantiation of the Bregman distance between probability distributions. Moreover, we suggest some new algorithms for rule-based semi-supervised learning and show connections with harmonic functions and minimum multi-way cuts in graph-based semi-supervised learning.

Connected through hyperedge HE-1 (induced by source paper "label efficient semi supervised learning via graph filtering"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "graph neural networks a review of methods and applications"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "deep learning on graphs a survey"
    - "rethinking knowledge graph propagation for zero shot learning"
    - "graph partition neural networks for semi supervised classification"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"

Connected through hyperedge HE-2 (induced by source paper "biomedical named entity recognition via reference set augmented bootstrapping"):
  with members:
    - "nseen neural semantic embedding for entity normalization"
    - "learning named entity tagger using domain specific dictionary"
    - "automated phrase mining from massive text corpora"
    - "snorkel rapid training data creation with weak supervision"
    - "swellshark a generative model for biomedical named entity recognition without labeled data"
    - "empower sequence labeling with task aware neural language model"
    - "neural architectures for named entity recognition"
    - "training deep neural networks on noisy labels with bootstrapping"

Connected through hyperedge HE-3 (induced by source paper "collective semi supervised learning for user profiling in social media"):
  with members:
    - "multiple graph regularized protein domain ranking"

Connected through hyperedge HE-4 (induced by source paper "training deep neural networks on noisy labels with bootstrapping"):
  with members:
    - "going deeper with convolutions"
    - "training convolutional networks with noisy labels"
    - "deepid net multi stage and deformable deep convolutional neural networks for object detection"
    - "scalable high quality object detection"
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "scalable object detection using deep neural networks"
    - "visualizing and understanding convolutional networks"
    - "deep generative stochastic networks trainable by backprop"

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)