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: "paraphrase thought sentence embedding module imitating human language recognition"
  Abstract: Sentence embedding is an important research topic in natural language processing. It is essential to generate a good embedding vector that fully reflects the semantic meaning of a sentence in order to achieve an enhanced performance for various natural language processing tasks, such as machine translation and document classification. Thus far, various sentence embedding models have been proposed, and their feasibility has been demonstrated through good performances on tasks following embedding, such as sentiment analysis and sentence classification. However, because the performances of sentence classification and sentiment analysis can be enhanced by using a simple sentence representation method, it is not sufficient to claim that these models fully reflect the meanings of sentences based on good performances for such tasks. In this paper, inspired by human language recognition, we propose the following concept of semantic coherence, which should be satisfied for a good sentence embedding method: similar sentences should be located close to each other in the embedding space. Then, we propose the Paraphrase-Thought (P-thought) model to pursue semantic coherence as much as possible. Experimental results on two paraphrase identification datasets (MS COCO and STS benchmark) show that the P-thought models outperform the benchmarked sentence embedding methods.

Connected through hyperedge HE-1 (induced by source paper "sentence transition matrix an efficient approach that preserves sentence semantics"):
  with members:
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "unsupervised cross lingual word embedding by multilingual neural language models"
    - "unsupervised cross lingual transfer of word embedding spaces"
    - "unsupervised machine translation using monolingual corpora only"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "unsupervised neural machine translation"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "a deep generative framework for paraphrase generation"

Connected through hyperedge HE-2 (induced by source paper "bioconceptvec creating and evaluating literature based biomedical concept embeddings on a large scale"):
  with members:
    - "relation extraction between the clinical entities based on the shortest dependency path based lstm"
    - "biomedical mention disambiguation using a deep learning approach"
    - "reflex flexible framework for relation extraction in multiple domains"
    - "deep learning with sentence embeddings pre trained on biomedical corpora improves the performance of finding similar sentences in electronic medical records"
    - "concept based embeddings for natural language processing"
    - "hierarchical neural networks for sequential sentence classification in medical scientific abstracts"
    - "medical concept embedding with time aware attention"
    - "clinical concept embeddings learned from massive sources of multimodal medical data"

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)