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 "sieve helping developers sift wheat from chaff via cross platform analysis"):
  Abstract: Software developers have benefited from various sources of knowledge such as forums, question-and-answer sites, and social media platforms to help them in various tasks. Extracting software-related knowledge from different platforms involves many challenges. In this paper, we propose an approach to improve the effectiveness of knowledge extraction tasks by performing cross-platform analysis. Our approach is based on transfer representation learning and word embeddings, leveraging information extracted from a source platform which contains rich domain-related content. The information extracted is then used to solve tasks in another platform (considered as target platform) with less domain-related contents. We first build a word embeddings model as a representation learned from the source platform, and use the model to improve the performance of knowledge extraction tasks in the target platform. We experiment with Software Engineering Stack Exchange and Stack Overflow as source platforms, and two different target platforms, i.e., Twitter and YouTube. Our experiments show that our approach improves performance of existing work for the tasks of identifying software-related tweets and helpful YouTube comments.

Includes members:
  Member "a practitioners guide to transfer learning for text classification using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards better social crisis data with hermes hybrid sensing for emergency management system")
  Member "strict information retrieval based search term identification for concept location":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "recommending comprehensive solutions for programming tasks by mining crowd knowledge")
  Member "sentiment polarity detection for software development":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sentimoji an emoji powered learning approach for sentiment analysis in software engineering")
  Member "semantically enhanced software traceability using deep learning techniques":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "software engineering meets deep learning a literature review")
  Member "modelling domain relationships for transfer learning on retrieval based question answering systems in e commerce":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "iart intent aware response ranking with transformers in information seeking conversation systems")
  Member "transfer learning for named entity recognition with neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "zero resource cross domain named entity recognition")
  Member "enriching word vectors with subword information":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "how transferable are neural networks in nlp applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "temporal embeddings and transformer models for narrative text understanding")

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.IR(Information Retrieval)