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 "machine translation evaluation meets community question answering"):
  Abstract: We explore the applicability of machine translation evaluation (MTE) methods to a very different problem: answer ranking in community Question Answering. In particular, we adopt a pairwise neural network (NN) architecture, which incorporates MTE features, as well as rich syntactic and semantic embeddings, and which efficiently models complex non-linear interactions. The evaluation results show state-of-the-art performance, with sizeable contribution from both the MTE features and from the pairwise NN architecture.

Includes members:
  Member "semeval 2016 task 3 community question answering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "review guided helpful answer identification in e commerce")
  Member "semanticz at semeval 2016 task 3 ranking relevant answers in community question answering using semantic similarity based on fine tuned word embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "automatic fact checking using context and discourse information")
  Member "pairwise neural machine translation evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sentence level human translation quality estimation with attention based neural networks")
  Member "semi supervised question retrieval with gated convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised question answering by cloze translation")
  Member "applying deep learning to answer selection a study and an open task":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hybrid tiled convolutional neural networks for text sentiment classification")
  Member "word embedding based correlation model for question answer matching":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "enhancing clinical concept extraction with contextual embeddings")
  Member "lstm based deep learning models for non factoid answer selection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi level head wise match and aggregation in transformer for textual sequence matching")

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)