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: "attention based pairwise multi perspective convolutional neural network for answer selection in question answering"
  Abstract: Over the past few years, question answering and information retrieval systems have become widely used. These systems attempt to find the answer of the asked questions from raw text sources. A component of these systems is Answer Selection which selects the most relevant answer from candidate answers. Syntactic similarities were mostly used to compute the similarity, but in recent works, deep neural networks have been used which have made a significant improvement in this field. In this research, a model is proposed to select the most relevant answers to the factoid question from the candidate answers. The proposed model ranks the candidate answers in terms of semantic and syntactic similarity to the question, using convolutional neural networks. In this research, Attention mechanism and Sparse feature vector use the context-sensitive interactions between questions and answer sentence. Wide convolution increases the importance of the interrogative word. Pairwise ranking is used to learn differentiable representations to distinguish positive and negative answers. Our model indicates strong performance on the TrecQA beating previous state-of-the-art systems by 2.62% in MAP and 2.13% in MRR while using the benefits of no additional syntactic parsers and external tools. The results show that using context-sensitive interactions between question and answer sentences can help to find the correct answer more accurately.

Connected through hyperedge HE-1 (induced by source paper "bas an answer selection method using bert language model"):
  with members:
    - "a compare aggregate model with latent clustering for answer selection"
    - "albert a lite bert for self supervised learning of language representations"
    - "deep learning using rectified linear units relu"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "recurrent additive networks"
    - "exploring the effectiveness of convolutional neural networks for answer selection in end to end question answering"
    - "applying deep learning to answer selection a study and an open task"

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)