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: "training a neural network in a low resource setting on automatically annotated noisy data"
  Abstract: Manually labeled corpora are expensive to create and often not available for low-resource languages or domains. Automatic labeling approaches are an alternative way to obtain labeled data in a quicker and cheaper way. However, these labels often contain more errors which can deteriorate a classifier's performance when trained on this data. We propose a noise layer that is added to a neural network architecture. This allows modeling the noise and train on a combination of clean and noisy data. We show that in a low-resource NER task we can improve performance by up to 35% by using additional, noisy data and handling the noise.

Connected through hyperedge HE-1 (induced by source paper "distant supervision and noisy label learning for low resource named entity recognition a study on hausa and yor ub a"):
  with members:
    - "named entity recognition with partially annotated training data"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "low resource name tagging learned with weakly labeled data"
    - "cloze driven pretraining of self attention networks"
    - "self learning to filter noisy labels with self ensembling"
    - "image classification with deep learning in the presence of noisy labels a survey"
    - "feature dependent confusion matrices for low resource ner labeling with noisy labels"
    - "massively multilingual transfer for ner"

Connected through hyperedge HE-2 (induced by source paper "feature dependent confusion matrices for low resource ner labeling with noisy labels"):
  with members:
    - "multilingual ner transfer for low resource languages"
    - "handling noisy labels for robustly learning from self training data for low resource sequence labeling"
    - "learning word vectors for 157 languages"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "learning with noise enhance distantly supervised relation extraction with dynamic transition matrix"
    - "learning from noisy large scale datasets with minimal supervision"
    - "learning when to trust distant supervision an application to low resource pos tagging using cross lingual projection"
    - "training convolutional networks with noisy labels"

Connected through hyperedge HE-3 (induced by source paper "handling noisy labels for robustly learning from self training data for low resource sequence labeling"):
  with members:
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "learning with noise enhance distantly supervised relation extraction with dynamic transition matrix"
    - "learning from noisy large scale datasets with minimal supervision"
    - "an overview of multi task learning in deep neural networks"
    - "polyglot distributed word representations for multilingual nlp"
    - "introduction to the conll 2000 shared task chunking"

Connected through hyperedge HE-4 (induced by source paper "named entity recognition with partially annotated training data"):
  with members:
    - "exploiting partially annotated data for temporal relation extraction"
    - "snorkel rapid training data creation with weak supervision"
    - "swellshark a generative model for biomedical named entity recognition without labeled data"
    - "fidelity weighted learning"
    - "neural architectures for named entity recognition"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

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)