human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "a sentence meaning based alignment method for parallel text corpora preparation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "polish english speech statistical machine translation systems for the iwslt 2014")
  Member "real time statistical speech translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "polish english speech statistical machine translation systems for the iwslt 2014")
  Member "polish english speech statistical machine translation systems for the iwslt 2013":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "polish english speech statistical machine translation systems for the iwslt 2014")

Question: Please tell me which class the source paper 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)