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: "medical synonym extraction with concept space models"
  Abstract: In this paper, we present a novel approach for medical synonym extraction. We aim to integrate the term embedding with the medical domain knowledge for healthcare applications. One advantage of our method is that it is very scalable. Experiments on a dataset with more than 1M term pairs show that the proposed approach outperforms the baseline approaches by a large margin.

Connected through hyperedge HE-1 (induced by source paper "surfcon synonym discovery on privacy aware clinical data"):
  with members:
    - "synonymnet multi context bilateral matching for entity synonyms"
    - "mining entity synonyms with efficient neural set generation"
    - "automatic synonym discovery with knowledge bases"
    - "attention is all you need"
    - "semi supervised embedding in attributed networks with outliers"
    - "enriching word vectors with subword information"
    - "a joint many task model growing a neural network for multiple nlp tasks"
    - "charagram embedding words and sentences via character n grams"

Connected through hyperedge HE-2 (induced by source paper "an enhanced computational feature selection method for medical synonym identification via bilingualism and multi corpus training"):
  with members:
    - "distant ie by bootstrapping using lists and document structure"
    - "component enhanced chinese character embeddings"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "the google similarity distance"

Connected through hyperedge HE-3 (induced by source paper "bidirectional recurrent neural networks for medical event detection in electronic health records"):
  with members:
    - "bidirectional lstm crf models for sequence tagging"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

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)