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 "overcoming low utility facets for complex answer retrieval"):
  Abstract: Many questions cannot be answered simply; their answers must include numerous nuanced details and context. Complex Answer Retrieval (CAR) is the retrieval of answers to such questions. These questions can be constructed from a topic entity (e.g., ‘cheese’) and a facet (e.g., ‘health effects’). While topic matching has been thoroughly explored, we observe that some facets use general language that is unlikely to appear verbatim in answers, exhibiting low utility. In this work, we present an approach to CAR that identifies and addresses low-utility facets. First, we propose two estimators of facet utility: the hierarchical structure of CAR queries, and facet frequency information from training data. Then, to improve the retrieval performance on low-utility headings, we include entity similarity scores using embeddings trained from a CAR knowledge graph, which captures the context of facets. We show that our methods are effective by applying them to two leading neural ranking techniques, and evaluating them on the TREC CAR dataset. We find that our approach perform significantly better than the unmodified neural ranker and other leading CAR techniques, yielding state-of-the-art results. We also provide a detailed analysis of our results, verify that low-utility facets are indeed difficult to match, and that our approach improves the performance for these difficult queries.

Includes members:
  Member "characterizing question facets for complex answer retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sector a neural model for coherent topic segmentation and classification")
  Member "deeprank a new deep architecture for relevance ranking in information retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "selective weak supervision for neural information retrieval")
  Member "end to end neural ad hoc ranking with kernel pooling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "selective weak supervision for neural information retrieval")
  Member "neural ranking models with multiple document fields":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "duet at trec 2019 deep learning track")
  Member "co pacrr a context aware neural ir model for ad hoc retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "interpretable time budget constrained contextualization for re ranking")
  Member "word entity duet representations for document ranking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "selective weak supervision for neural information retrieval")
  Member "benchmark for complex answer retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning contextualized document representations for healthcare answer retrieval")
  Member "pacrr a position aware neural ir model for relevance matching":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "selective weak supervision for neural information retrieval")

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)