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: "automatic severity classification of coronary artery disease via recurrent capsule network"
  Abstract: Coronary artery disease (CAD) is one of the leading causes of cardiovascular disease deaths. CAD condition progresses rapidly, if not diagnosed and treated at an early stage may eventually lead to an irreversible state of the heart muscle death. Invasive coronary arteriography is the gold standard technique for CAD diagnosis. Coronary arteriography texts describe which part has stenosis and how much stenosis is in details. It is crucial to conduct the severity classification of CAD. In this paper, we employ a recurrent capsule network (RCN) to extract semantic relations between clinical named entities in Chinese coronary arteriography texts, through which we can automatically find out the maximal stenosis for each lumen to inference how severe CAD is according to the improved method of Gensini. Experimental results on the corpus collected from Shanghai Shuguang Hospital show that our proposed method achieves an accuracy of 97.0\% in the severity classification of CAD.

Connected through hyperedge HE-1 (induced by source paper "fine tuning bert for joint entity and relation extraction in chinese medical text"):
  with members:
    - "fine grained named entity recognition using elmo and wikidata"
    - "improving relation extraction by pre trained language representations"
    - "deep contextualized word representations"
    - "attention is all you need"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "a survey of deep learning methods for relation extraction"
    - "relation extraction from clinical texts using domain invariant convolutional neural network"
    - "end to end relation extraction using lstms on sequences and tree structures"

Connected through hyperedge HE-2 (induced by source paper "multi labeled relation extraction with attentive capsule network"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "ensemble neural relation extraction with adaptive boosting"
    - "neural relation extraction via inner sentence noise reduction and transfer learning"
    - "dynamic routing between capsules"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "combining neural networks and log linear models to improve relation extraction"
    - "classifying relations by ranking with convolutional neural networks"
    - "relation classification via recurrent neural network"

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)