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: "an unsupervised model with attention autoencoders for question retrieval"
  Abstract: Question retrieval is a crucial subtask for community question answering. Previous research focus on supervised models which depend heavily on training data and manual feature engineering. In this paper, we propose a novel unsupervised framework, namely reduced attentive matching network (RAMN), to compute semantic matching between two questions. Our RAMN integrates together the deep semantic representations, the shallow lexical mismatching information and the initial rank produced by an external search engine. For the first time, we propose attention autoencoders to generate semantic representations of questions. In addition, we employ lexical mismatching to capture surface matching between two questions, which is derived from the importance of each word in a question. We conduct experiments on the open CQA datasets of SemEval-2016 and SemEval-2017. The experimental results show that our unsupervised model obtains comparable performance with the state-of-the-art supervised methods in SemEval-2016 Task 3, and outperforms the best system in SemEval-2017 Task 3 by a wide margin.

Connected through hyperedge HE-1 (induced by source paper "neural duplicate question detection without labeled training data"):
  with members:
    - "semeval 2017 task 3 community question answering"
    - "roberta a robustly optimized bert pretraining approach"
    - "adversarial domain adaptation for duplicate question detection"
    - "tensor2tensor for neural machine translation"
    - "the natural language decathlon multitask learning as question answering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "injecting relational structural representation in neural networks for question similarity"
    - "harvesting paragraph level question answer pairs from wikipedia"

Connected through hyperedge HE-2 (induced by source paper "adversarial domain adaptation for duplicate question detection"):
  with members:
    - "semeval 2016 task 3 community question answering"
    - "semeval 2017 task 3 community question answering"
    - "injecting relational structural representation in neural networks for question similarity"
    - "aspect augmented adversarial networks for domain adaptation"
    - "modelling domain relationships for transfer learning on retrieval based question answering systems in e commerce"
    - "adversarial deep averaging networks for cross lingual sentiment classification"
    - "semi supervised question retrieval with gated convolutions"
    - "effective approaches to attention based neural machine translation"

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)