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: "noisy parallel and comparable corpora filtering methodology for the extraction of bi lingual equivalent data at sentence level"
  Abstract: Text alignment and text quality are critical to the accuracy of Machine Trans- lation (MT) systems, some NLP tools, and any other text processing tasks requiring bilingual data. This research proposes a language-independent bi- sentence filtering approach based on Polish (not a position-sensitive language) to English experiments. This cleaning approach was developed on the TED Talks corpus and also initially tested on the Wikipedia comparable corpus, but it can be used for any text domain or language pair. The proposed approach implements various heuristics for sentence comparison. Some of the heuristics leverage synonyms as well as semantic and structural analysis of text as additio- nal information. Minimization of data loss has been? ensured. An improvement in MT system scores with text processed using this tool is discussed.

Connected through hyperedge HE-1 (induced by source paper "coursera corpus mining and multistage fine tuning for improving lectures translation"):
  with members:
    - "exploiting sentential context for neural machine translation"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "target conditioned sampling optimizing data selection for multilingual neural machine translation"
    - "exploiting out of domain parallel data through multilingual transfer learning for low resource neural machine translation"
    - "when a good translation is wrong in context context aware machine translation improves on deixis ellipsis and lexical cohesion"
    - "attention is all you need"
    - "an empirical comparison of simple domain adaptation methods for neural machine translation"
    - "neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "impact of corpora quality on neural machine translation"):
  with members:
    - "neural machine translation of rare words with subword units"

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)