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: "a multi task learning approach for improving product title compression with user search log data"
  Abstract: It is a challenging and practical research problem to obtain effective compression of lengthy product titles for E-commerce. This is particularly important as more and more users browse mobile E-commerce apps and more merchants make the original product titles redundant and lengthy for Search Engine Optimization. Traditional text summarization approaches often require a large amount of preprocessing costs and do not capture the important issue of conversion rate in E-commerce. This paper proposes a novel multi-task learning approach for improving product title compression with user search log data. In particular, a pointer network-based sequence-to-sequence approach is utilized for title compression with an attentive mechanism as an extractive method and an attentive encoder-decoder approach is utilized for generating user search queries. The encoding parameters (i.e., semantic embedding of original titles) are shared among the two tasks and the attention distributions are jointly optimized. An extensive set of experiments with both human annotated data and online deployment demonstrate the advantage of the proposed research for both compression qualities and online business values.

Connected through hyperedge HE-1 (induced by source paper "product knowledge graph embedding for e commerce"):
  with members:
    - "analogies explained towards understanding word embeddings"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "multi task learning as multi objective optimization"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "attention is all you need"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "poincar e embeddings for learning hierarchical representations"

Connected through hyperedge HE-2 (induced by source paper "multi modal generative adversarial network for short product title generation in mobile e commerce"):
  with members:
    - "automatic generation of chinese short product titles for mobile display"
    - "improving automatic source code summarization via deep reinforcement learning"
    - "get to the point summarization with pointer generator networks"
    - "adversarial learning for neural dialogue generation"
    - "adversarial ranking for language generation"
    - "long text generation via adversarial training with leaked information"
    - "towards diverse and natural image descriptions via a conditional gan"
    - "distraction based neural networks for document summarization"

Connected through hyperedge HE-3 (induced by source paper "multi source pointer network for product title summarization"):
  with members:
    - "get to the point summarization with pointer generator networks"
    - "a copy augmented sequence to sequence architecture gives good performance on task oriented dialogue"
    - "a deep reinforced model for abstractive summarization"
    - "pointing the unknown words"
    - "sequence to backward and forward sequences a content introducing approach to generative short text conversation"
    - "stop clickbait detecting and preventing clickbaits in online news media"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "incorporating copying mechanism in sequence to sequence learning"

Connected through hyperedge HE-4 (induced by source paper "automatic generation of chinese short product titles for mobile display"):
  with members:
    - "get to the point summarization with pointer generator networks"
    - "neural extractive summarization with side information"
    - "a deep reinforced model for abstractive summarization"
    - "wide deep learning for recommender systems"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "neural summarization by extracting sentences and words"
    - "sequence to sequence rnns for text summarization"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "product title refinement via multi modal generative adversarial learning"):
  with members:
    - "automatic generation of chinese short product titles for mobile display"
    - "get to the point summarization with pointer generator networks"
    - "adversarial learning for neural dialogue generation"
    - "distraction based neural networks for document summarization"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "attsum joint learning of focusing and summarization with neural attention"
    - "language as a latent variable discrete generative models for sentence compression"
    - "summarunner a recurrent neural network based sequence model for extractive summarization of documents"

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)