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: "neural networks for joint sentence classification in medical paper abstracts"
  Abstract: Existing models based on artificial neural networks (ANNs) for sentence classification often do not incorporate the context in which sentences appear, and classify sentences individually. However, traditional sentence classification approaches have been shown to greatly benefit from jointly classifying subsequent sentences, such as with conditional random fields. In this work, we present an ANN architecture that combines the effectiveness of typical ANN models to classify sentences in isolation, with the strength of structured prediction. Our model achieves state-of-the-art results on two different datasets for sequential sentence classification in medical abstracts.

Connected through hyperedge HE-1 (induced by source paper "hierarchical neural networks for sequential sentence classification in medical scientific abstracts"):
  with members:
    - "pubmed 200k rct a dataset for sequential sentence classification in medical abstracts"
    - "enriching word vectors with subword information"
    - "dependency sensitive convolutional neural networks for modeling sentences and documents"
    - "recurrent neural network for text classification with multi task learning"
    - "dropout with expectation linear regularization"
    - "bag of tricks for efficient text classification"
    - "neural architectures for named entity recognition"
    - "sequential short text classification with recurrent and convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "advancing pico element detection in medical text via deep neural networks"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "hierarchical neural networks for sequential sentence classification in medical scientific abstracts"
    - "attention is all you need"
    - "a structured self attentive sentence embedding"
    - "enriching word vectors with subword information"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "mit at semeval 2017 task 10 relation extraction with convolutional neural networks"):
  with members:
    - "comparing rule based and deep learning models for patient phenotyping"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-4 (induced by source paper "pubmed 200k rct a dataset for sequential sentence classification in medical abstracts"):
  with members:
    - "scikit learn machine learning in python"

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.CL(Computation and Language)