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: "faq based question answering via word alignment"
  Abstract: In this paper, we propose a novel word-alignment-based method to solve the FAQ-based question answering task. First, we employ a neural network model to calculate question similarity, where the word alignment between two questions is used for extracting features. Second, we design a bootstrap-based feature extraction method to extract a small set of effective lexical features. Third, we propose a learning-to-rank algorithm to train parameters more suitable for the ranking tasks. Experimental results, conducted on three languages (English, Spanish and Japanese), demonstrate that the question similarity model is more effective than baseline systems, the sparse features bring 5% improvements on top-1 accuracy, and the learning-to-rank algorithm works significantly better than the traditional method. We further evaluate our method on the answer sentence selection task. Our method outperforms all the previous systems on the standard TREC data set.

Connected through hyperedge HE-1 (induced by source paper "a gated self attention memory network for answer selection"):
  with members:
    - "a compare aggregate model with latent clustering for answer selection"
    - "deep contextualized word representations"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "question answering through transfer learning from large fine grained supervision data"
    - "attention is all you need"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "supervised and unsupervised transfer learning for question answering"

Connected through hyperedge HE-2 (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"
    - "attention based pairwise multi perspective convolutional neural network for answer selection in question answering"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "bilateral multi perspective matching for natural language sentences"):
  with members:
    - "attentive pooling networks"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "semi supervised clustering for short text via deep representation learning"
    - "long short term memory networks for machine reading"
    - "a decomposable attention model for natural language inference"
    - "sentence similarity learning by lexical decomposition and composition"
    - "enhanced lstm for natural language inference"
    - "modelling interaction of sentence pair with coupled lstms"

Connected through hyperedge HE-4 (induced by source paper "hungarian layer logics empowered neural architecture"):
  with members:
    - "efficient neural architecture search via parameter sharing"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "bilateral multi perspective matching for natural language sentences"
    - "pointing the unknown words"
    - "semi supervised clustering for short text via deep representation learning"
    - "sentence similarity learning by lexical decomposition and composition"
    - "dynamic neural turing machine with soft and hard addressing schemes"
    - "neural variational inference for text processing"

Connected through hyperedge HE-5 (induced by source paper "indirect supervision for relation extraction using question answer pairs"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "attentive pooling networks"
    - "distant supervision for relation extraction beyond the sentence boundary"
    - "improved relation classification by deep recurrent neural networks with data augmentation"
    - "cotype joint extraction of typed entities and relations with knowledge bases"
    - "improving information extraction by acquiring external evidence with reinforcement learning"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "improved relation extraction with feature rich compositional embedding models"

Connected through hyperedge HE-6 (induced by source paper "attentive pooling networks"):
  with members:
    - "applying deep learning to answer selection a study and an open task"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "convolutional neural network architectures for matching natural language sentences"
    - "reasoning about entailment with neural attention"
    - "teaching machines to read and comprehend"
    - "lstm based deep learning models for non factoid answer selection"
    - "deep learning for answer sentence selection"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-7 (induced by source paper "modeling relational information in question answer pairs with convolutional neural networks"):
  with members:
    - "neural variational inference for text processing"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "convolutional neural network architectures for matching natural language sentences"
    - "deep learning for answer sentence selection"
    - "modelling visualising and summarising documents with a single convolutional neural network"
    - "open question answering with weakly supervised embedding models"
    - "convolutional neural networks for sentence classification"
    - "a convolutional neural network for modelling sentences"

Connected through hyperedge HE-8 (induced by source paper "sentence similarity learning by lexical decomposition and composition"):
  with members:
    - "attentive pooling networks"
    - "effective approaches to attention based neural machine translation"
    - "neural variational inference for text processing"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "efficient estimation of word representations in vector space"

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)