human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "development of deep learning algorithms to categorize free text notes pertaining to diabetes convolution neural networks achieve higher accuracy than support vector machines"):
  Abstract: Health professionals can use natural language processing (NLP) technologies when reviewing electronic health records (EHR). Machine learning free-text classifiers can help them identify problems and make critical decisions. We aim to develop deep learning neural network algorithms that identify EHR progress notes pertaining to diabetes and validate the algorithms at two institutions. The data used are 2,000 EHR progress notes retrieved from patients with diabetes and all notes were annotated manually as diabetic or non-diabetic. Several deep learning classifiers were developed, and their performances were evaluated with the area under the ROC curve (AUC). The convolutional neural network (CNN) model with a separable convolution layer accurately identified diabetes-related notes in the Brigham and Womens Hospital testing set with the highest AUC of 0.975. Deep learning classifiers can be used to identify EHR progress notes pertaining to diabetes. In particular, the CNN-based classifier can achieve a higher AUC than an SVM-based classifier.

Includes members:
  Member "text classification improved by integrating bidirectional lstm with two dimensional max pooling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sentence level human translation quality estimation with attention based neural networks")
  Member "recurrent neural network for text classification with multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "meta embeddings based on self attention")
  Member "learning natural language inference using bidirectional lstm model and inner attention":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mhsan multi head self attention network for visual semantic embedding")
  Member "sequential short text classification with recurrent and convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "local contextual attention with hierarchical structure for dialogue act recognition")
  Member "a c lstm neural network for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "benchmark performance of machine and deep learning based methodologies for urdu text document classification")
  Member "speeding up convolutional neural networks with low rank expansions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "convolutional neural networks for sentence classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "a convolutional neural network for modelling sentences":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

Question: Please tell me which class the source paper 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)