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 "generating scientific question answering corpora from q a forums"):
  Abstract: Question Answering (QA) is a natural language processing task that aims at retrieving relevant answers to user questions. While much progress has been made in this area, biomedical questions are still a challenge to most QA approaches, due to the complexity of the domain and limited availability of training sets. We present a method to automatically extract question-article pairs from Q&A web forums, which can be used for document retrieval and QA tasks. The proposed framework extracts questions from selected forums as well as answers that contain citations that can be mapped to a unique entry of a digital library. This way, QA systems based on document retrieval can be developed and evaluated using the question-article pairs annotated by users of these forums. We generated the SciQA corpus by applying our framework to three forums, obtaining 5,432 questions and 10,208 question-article pairs. We evaluated how the number of articles associated with each question and the number of votes on each answer affects the performance of baseline document retrieval approaches. Also, we trained a state-of-the-art deep learning model that obtained higher scores in most test batches than a model trained only on a dataset manually annotated by experts. The framework described in this paper can be used to update the SciQA corpus from the same forums as new posts are made, and from other forums that support their answers with documents.

Includes members:
  Member "a question entailment approach to question answering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning contextualized document representations for healthcare answer retrieval")
  Member "emrqa a large corpus for question answering on electronic medical records":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "practical annotation strategies for question answering datasets")
  Member "biomedical question answering via weighted neural network passage retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dc3 a diagnostic case challenge collection for clinical decision support")
  Member "deeprank a new deep architecture for relevance ranking in information retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "selective weak supervision for neural information retrieval")
  Member "pacrr a position aware neural ir model for relevance matching":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "selective weak supervision for neural information retrieval")
  Member "a deep relevance matching model for ad hoc retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "iart intent aware response ranking with transformers in information seeking conversation systems")

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)