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: "a sentence meaning based alignment method for parallel text corpora preparation"
  Abstract: Text alignment is crucial to the accuracy of Machine Translation (MT) systems, some NLP tools or any other text processing tasks requiring bilingual data. This research proposes a language independent sentence alignment approach based on Polish (not position-sensitive language) to English experiments. This alignment approach was developed on the TED Talks corpus, but can be used for any text domain or language pair. The proposed approach implements various heuristics for sentence recognition. Some of them value synonyms and semantic text structure analysis as a part of additional information. Minimization of data loss was ensured. The solution is compared to other sentence alignment implementations. Also an improvement in MT system score with text processed with described tool is shown.

Connected through hyperedge HE-1 (induced by source paper "polish english speech statistical machine translation systems for the iwslt 2014"):
  with members:
    - "real time statistical speech translation"
    - "enhanced bilingual evaluation understudy"
    - "neural machine translation by jointly learning to align and translate"
    - "polish english speech statistical machine translation systems for the iwslt 2013"

Connected through hyperedge HE-2 (induced by source paper "noisy parallel and comparable corpora filtering methodology for the extraction of bi lingual equivalent data at sentence level"):
  with members:
    - "real time statistical speech translation"
    - "polish english speech statistical machine translation systems for the iwslt 2013"

Connected through hyperedge HE-3 (induced by source paper "pjait systems for the iwslt 2015 evaluation campaign enhanced by comparable corpora"):
  with members:
    - "tuned and gpu accelerated parallel data mining from comparable corpora"

Connected through hyperedge HE-4 (induced by source paper "building subject aligned comparable corpora and mining it for truly parallel sentence pairs"):
  with members:
    - "real time statistical speech translation"
    - "polish english speech statistical machine translation systems for the iwslt 2013"

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)