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 machine translation framework based cross lingual document vector with distance constraint training"
  Abstract: A universal cross-lingual representation of documents is very important for many natural language processing tasks. In this paper, we present a document vectorization method which can effectively create document vectors via self-attention mechanism using a neural machine translation (NMT) framework. The model used by our method can be trained with parallel corpora that are unrelated to the task at hand. During testing, our method will take a monolingual document and convert it into a "Neural machine Translation framework based crosslingual Document Vector with distance constraint training" (cNTDV). cNTDV is a follow-up study from our previous research on the neural machine translation framework based document vector. The cNTDV can produce the document vector from a forward-pass of the encoder with fast speed. Moreover, it is trained with a distance constraint, so that the document vector obtained from different language pair is always consistent with each other. In a cross-lingual document classification task, our cNTDV embeddings surpass the published state-of-the-art performance in the English-to-German classification test, and, to our best knowledge, it also achieves the second best performance in German-to-English classification test. Comparing to our previous research, it does not need a translator in the testing process, which makes the model faster and more convenient.

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)