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: "automatic generation of chinese short product titles for mobile display"
  Abstract: This paper studies the problem of automatically extracting a short title from a manually written longer description of E-commerce products for display on mobile devices. It is a new extractive summarization problem on short text inputs, for which we pro- pose a feature-enriched network model, combining three different categories of features in parallel. Experimental results show that our framework significantly outperforms several baselines by a substantial gain of 4.5%. Moreover, we produce an extractive summarization dataset for E-commerce short texts and will release it to the research community.

Connected through hyperedge HE-1 (induced by source paper "multi modal generative adversarial network for short product title generation in mobile e commerce"):
  with members:
    - "a multi task learning approach for improving product title compression with user search log data"
    - "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-2 (induced by source paper "exact k recommendation via maximal clique optimization"):
  with members:
    - "a multi task selected learning approach for solving 3d flexible bin packing problem"
    - "query tracking for e commerce conversational search a machine comprehension perspective"
    - "learning a deep listwise context model for ranking refinement"
    - "representing verbs as argument concepts"
    - "a brand level ranking system with the customized attention gru model"
    - "deep cascade multi task learning for slot filling in online shopping assistant"
    - "behavioral cloning from observation"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "product title refinement via multi modal generative adversarial learning"):
  with members:
    - "a multi task learning approach for improving product title compression with user search log data"
    - "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)