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: "review driven answer generation for product related questions in e commerce"
  Abstract: The users often have many product-related questions before they make a purchase decision in E-commerce. However, it is often time-consuming to examine each user review to identify the desired information. In this paper, we propose a novel review-driven framework for answer generation for product-related questions in E-commerce, named RAGE. We develope RAGE on the basis of the multi-layer convolutional architecture to facilitate speed-up of answer generation with the parallel computation. For each question, RAGE first extracts the relevant review snippets from the reviews of the corresponding product. Then, we devise a mechanism to identify the relevant information from the noise-prone review snippets and incorporate this information to guide the answer generation. The experiments on two real-world E-Commerce datasets show that the proposed RAGE significantly outperforms the existing alternatives in producing more accurate and informative answers in natural language. Moreover, RAGE takes much less time for both model training and answer generation than the existing RNN based generation models.

Connected through hyperedge HE-1 (induced by source paper "quantifying and alleviating the language prior problem in visual question answering"):
  with members:
    - "mmalfm explainable recommendation by leveraging reviews and images"
    - "learning to count objects in natural images for visual question answering"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "overcoming language priors in visual question answering with adversarial regularization"
    - "visual question answering datasets algorithms and future challenges"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "bottom up and top down attention for image captioning and visual question answering"

Connected through hyperedge HE-2 (induced by source paper "improving multi turn dialogue modelling with utterance rewriter"):
  with members:
    - "sentence rewriting for semantic parsing"
    - "cota improving the speed and accuracy of customer support through ranking and deep networks"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "an exploration of neural sequence to sequence architectures for automatic post editing"
    - "end to end neural coreference resolution"
    - "quickedit editing text translations by crossing words out"
    - "a knowledge grounded neural conversation model"

Connected through hyperedge HE-3 (induced by source paper "recurrent point processes for dynamic review models"):
  with members:
    - "structured neural topic models for reviews"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "transnets learning to transform for recommendation"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "neural variational inference for text processing"
    - "continuous time dynamic topic models"

Connected through hyperedge HE-4 (induced by source paper "attentive long short term preference modeling for personalized product search"):
  with members:
    - "mmalfm explainable recommendation by leveraging reviews and images"
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "ensemble methods for personalized e commerce search challenge at cikm cup 2016"
    - "neural collaborative filtering"
    - "neural attentive session based recommendation"
    - "attentional factorization machines learning the weight of feature interactions via attention networks"
    - "a deep relevance matching model for ad hoc retrieval"

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)