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 "are abstracts enough for hypothesis generation"):
  Abstract: The potential for automatic hypothesis generation (HG) systems to improve research productivity keeps pace with the growing set of publicly available scientific information. But as data becomes easier to acquire, we must understand the effect different textual data sources have on our resulting hypotheses. Are abstracts enough for HG, or does it need full-text papers? How many papers does an HG system need to make valuable predictions? What effect do corpus size and document length have on HG results? To answer these questions we train multiple versions of knowledge network-based HG system, Moliere, on varying corpora in order to compare challenges and tradeoffs in terms of result quality and computational requirements. Moliere generalizes main principles of similar knowledge network-based HG systems and reinforces them with topic modeling components. The corpora include the abstract and full-text versions of PubMed Central, as well as iterative halves of MEDLINE, which allows us to compare the effect document length and count has on the results. We find that corpora with a higher median document length result in higher quality results, yet require substantially longer to process. Additionally, we find that the effect of document length is greater than that of document count, even if both sets contain only paper abstracts. Reproducibility: Our code can be found at github.com/JSybrandt/moliere, and our data is hosted at bit.ly/2GxghpM.

Includes members:
  Member "validation and topic driven ranking for biomedical hypothesis generation systems":
    also connected through other hyperedges:
      (none)
  Member "large scale validation of hypothesis generation systems via candidate ranking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "agatha automatic graph mining and transformer based hypothesis generation approach")
  Member "automated phrase mining from massive text corpora":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cube2net efficient query specific network construction with data cube organization")
  Member "moliere automatic biomedical hypothesis generation system":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cbag conditional biomedical abstract generation")
  Member "fasttext zip compressing text classification models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "aibench an agile domain specific benchmarking methodology and an ai benchmark suite")
  Member "bag of tricks for efficient text classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "description based text classification with reinforcement learning")
  Member "scalable topical phrase mining from text corpora":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cate category name guidedword embedding")
  Member "efficient estimation of word representations in vector space":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on contextual embeddings")

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)