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 "putting question answering systems into practice transfer learning for efficient domain customization"):
  Abstract: Traditional information retrieval (such as that offered by web search engines) impedes users with information overload from extensive result pages and the need to manually locate the desired information therein. Conversely, question-answering systems change how humans interact with information systems: users can now ask specific questions and obtain a tailored answer—both conveniently in natural language. Despite obvious benefits, their use is often limited to an academic context, largely because of expensive domain customizations, which means that the performance in domain-specific applications often fails to meet expectations. This article proposes cost-efficient remedies: (i) we leverage metadata through a filtering mechanism, which increases the precision of document retrieval, and (ii) we develop a novel fuse-and-oversample approach for transfer learning to improve the performance of answer extraction. Here, knowledge is inductively transferred from related, yet different, tasks to the domain-specific application, while accounting for potential differences in the sample sizes across both tasks. The resulting performance is demonstrated with actual use cases from a finance company and the film industry, where fewer than 400 question-answer pairs had to be annotated to yield significant performance gains. As a direct implication to management, this presents a promising path to better leveraging of knowledge stored in information systems.

Includes members:
  Member "deep learning in business analytics and operations research models applications and managerial implications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sentiment analysis based on rhetorical structure theory learning deep neural networks from discourse trees")
  Member "deep learning for affective computing text based emotion recognition in decision support":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sentiment analysis based on rhetorical structure theory learning deep neural networks from discourse trees")
  Member "adaptive document retrieval for deep question answering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a study on efficiency accuracy and document structure for answer sentence selection")
  Member "deep bayesian active learning for natural language processing results of a large scale empirical study":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "domain independent extraction of scientific concepts from research articles")
  Member "evidence aggregation for answer re ranking in open domain question answering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "decision support from financial disclosures with deep neural networks and transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a bert based sentiment analysis and key entity detection approach for online financial texts")
  Member "reading wikipedia to answer open domain questions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "description based text classification with reinforcement learning")
  Member "program induction by rationale generation learning to solve and explain algebraic word problems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs")

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)