human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "multi task learning with multi view attention for answer selection and knowledge base question answering"):
  Abstract: Answer selection and knowledge base question answering (KBQA) are two important tasks of question answering (QA) systems. Existing methods solve these two tasks separately, which requires large number of repetitive work and neglects the rich correlation information between tasks. In this paper, we tackle answer selection and KBQA tasks simultaneously via multi-task learning (MTL), motivated by the following motivations. First, both answer selection and KBQA can be regarded as a ranking problem, with one at text-level while the other at knowledge-level. Second, these two tasks can benefit each other: answer selection can incorporate the external knowledge from knowledge base (KB), while KBQA can be improved by learning contextual information from answer selection. To fulfill the goal of jointly learning these two tasks, we propose a novel multi-task learning scheme that utilizes multi-view attention learned from various perspectives to enable these tasks to interact with each other as well as learn more comprehensive sentence representations. The experiments conducted on several real-world datasets demonstrate the effectiveness of the proposed method, and the performance of answer selection and KBQA is improved. Also, the multi-view attention scheme is proved to be effective in assembling attentive information from different representational perspectives.

Includes members:
  Member "meta multi task learning for sequence modeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to few shot learn across diverse natural language classification tasks")
  Member "modeling semantics with gated graph neural networks for knowledge base question answering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "knowledge based conversational search")
  Member "soft layer specific multi task summarization with entailment and question generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to multi task learn for better neural machine translation")
  Member "same representation different attentions shareable sentence representation learning from multiple tasks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "leveraging code generation to improve code retrieval and summarization via dual learning")
  Member "improved neural relation detection for knowledge base question answering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural relation prediction for simple question answering over knowledge graph")
  Member "learning to rank question answer pairs with holographic dual lstm architecture":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "text based question answering from information retrieval and deep neural network perspectives a survey")
  Member "cross temporal recurrent networks for ranking question answer pairs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gated group self attention for answer selection")
  Member "attentive pooling networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cut based graph learning networks to discover compositional structure of sequential video data")

Question: Please tell me which class the source paper 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)