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 "adversarial domain adaptation for duplicate question detection"):
  Abstract: We address the problem of detecting duplicate questions in forums, which is an important step towards automating the process of answering new questions. As finding and annotating such potential duplicates manually is very tedious and costly, automatic methods based on machine learning are a viable alternative. However, many forums do not have annotated data, i.e., questions labeled by experts as duplicates, and thus a promising solution is to use domain adaptation from another forum that has such annotations. Here we focus on adversarial domain adaptation, deriving important findings about when it performs well and what properties of the domains are important in this regard. Our experiments with StackExchange data show an average improvement of 5.6% over the best baseline across multiple pairs of domains.

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 "semeval 2017 task 3 community question answering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a pilot study on multiple choice machine reading comprehension for vietnamese texts")
  Member "injecting relational structural representation in neural networks for question similarity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural duplicate question detection without labeled training data")
  Member "an unsupervised model with attention autoencoders for question retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neural duplicate question detection without labeled training data")
  Member "aspect augmented adversarial networks for domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised adversarial domain adaptation for implicit discourse relation classification")
  Member "modelling domain relationships for transfer learning on retrieval based question answering systems in e commerce":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "iart intent aware response ranking with transformers in information seeking conversation systems")
  Member "adversarial deep averaging networks for cross lingual sentiment classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "calibration of pre trained transformers")
  Member "semi supervised question retrieval with gated convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unsupervised question answering by cloze translation")

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)