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 "remedies against the vocabulary gap in information retrieval"):
  Abstract: Search engines rely heavily on term-based approaches that represent queries and documents as bags of words. Text—a document or a query—is represented by a bag of its words that ignores grammar and word order, but retains word frequency counts. When presented with a search query, the engine then ranks documents according to their relevance scores by computing, among other things, the matching degrees between query and document terms. While term-based approaches are intuitive and effective in practice, they are based on the hypothesis that documents that exactly contain the query terms are highly relevant regardless of query semantics. Inversely, term-based approaches assume documents that do not contain query terms as irrelevant. However, it is known that a high matching degree at the term level does not necessarily mean high relevance and, vice versa, documents that match null query terms may still be relevant. Consequently, there exists a vocabulary gap between queries and documents that occurs when both use different words to describe the same concepts. It is the alleviation of the effect brought forward by this vocabulary gap that is the topic of this dissertation. More specifically, we propose (1) methods to formulate an effective query from complex textual structures and (2) latent vector space models that circumvent the vocabulary gap in information retrieval.

Includes members:
  Member "neural vector spaces for unsupervised information retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a deep look into neural ranking models for information retrieval")
  Member "semantic entity retrieval toolkit":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural ir meets graph embedding a ranking model for product search")
  Member "pyndri a python interface to the indri search engine":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "macaw an extensible conversational information seeking platform")
  Member "reply with proactive recommendation of email attachments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neural networks for information retrieval")
  Member "unsupervised efficient and semantic expertise retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "quality aware skill translation models for expert finding on stackoverflow")
  Member "recurrent memory network for language modeling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reflective decoding network for image captioning")
  Member "learning latent vector spaces for product search":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "quality aware skill translation models for expert finding on stackoverflow")
  Member "adadelta an adaptive learning rate method":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hyper parameter optimization a review of algorithms and applications")

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)