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 "a study of factuality objectivity and relevance three desiderata in large scale information retrieval"):
  Abstract: Much of the information processed by Information Retrieval (IR) systems is unreliable, biased, and generally untrustworthy [1], [2], [3]. Yet, factuality & objectivity detection is not a standard component of IR systems, even though it has been possible in Natural Language Processing (NLP) in the last decade. Motivated by this, we ask if and how factuality & objectivity detection may benefit IR. We answer this in two parts. First, we use state-of-the-art NLP to compute the probability of document factuality & objectivity in two TREC collections, and analyse its relation to document relevance. We find that factuality is strongly and positively correlated to document relevance, but objectivity is not. Second, we study the impact of factuality & objectivity to retrieval effectiveness by treating them as query independent features that we combine with a competitive language modelling baseline. Experiments with 450 TREC queries show that factuality improves precision >10% over strong baselines, especially for uncurated data used in web search; objectivity gives mixed results. An overall clear trend is that document factuality & objectivity is much more beneficial to IR when searching uncurated (e.g. web) documents vs. curated (e.g. state documentation and newswire articles). To our knowledge, this is the first study of factuality & objectivity for back-end IR, contributing novel findings about the relation between relevance and factuality/objectivity, and statistically significant gains to retrieval effectiveness in the competitive web search task.

Includes members:
  Member "rhetorical relations for information retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discourse in multimedia a case study in information extraction")
  Member "exploiting the bipartite structure of entity grids for document coherence and retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "weighted bipartite or directed stream graphs for the modeling of temporal networks")
  Member "multi dueling bandits and their application to online ranker evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "regret minimization in stochastic contextual dueling bandits")
  Member "an improved multileaving algorithm for online ranker evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the value of bandit feedback for offline recommender system evaluation")
  Member "entropy and graph based modelling of document coherence using discourse entities an application":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dependencies formalising semantic catenae for information retrieval")
  Member "non compositional term dependence for information retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the compositionality prediction of noun phrases using poincar e embeddings")
  Member "efficient and effective spam filtering and re ranking for large web datasets":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "abstractive snippet generation")

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)