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 "selective weak supervision for neural information retrieval"):
  Abstract: This paper democratizes neural information retrieval to scenarios where large scale relevance training signals are not available. We revisit the classic IR intuition that anchor-document relations approximate query-document relevance and propose a reinforcement weak supervision selection method, ReInfoSelect, which learns to select anchor-document pairs that best weakly supervise the neural ranker (action), using the ranking performance on a handful of relevance labels as the reward. Iteratively, for a batch of anchor-document pairs, ReInfoSelect back propagates the gradients through the neural ranker, gathers its NDCG reward, and optimizes the data selection network using policy gradients, until the neural ranker's performance peaks on target relevance metrics (convergence). In our experiments on three TREC benchmarks, neural rankers trained by ReInfoSelect, with only publicly available anchor data, significantly outperform feature-based learning to rank methods and match the effectiveness of neural rankers trained with private commercial search logs. Our analyses show that ReInfoSelect effectively selects weak supervision signals based on the stage of the neural ranker training, and intuitively picks anchor-document pairs similar to query-document pairs.

Includes members:
  Member "latent retrieval for weakly supervised open domain question answering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "differentiable reasoning over a virtual knowledge base")
  Member "passage re ranking with bert":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a study on efficiency accuracy and document structure for answer sentence selection")
  Member "generic intent representation in web search":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised common question generation from multiple documents using reinforced contrastive coordinator")
  Member "document expansion by query prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "interpretable time budget constrained contextualization for re ranking")
  Member "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "document ranking with a pretrained sequence to sequence model")
  Member "deeper text understanding for ir with contextual neural language modeling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "kernel graph attention network for fact verification")
  Member "understanding the behaviors of bert in ranking":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a study on efficiency accuracy and document structure for answer sentence selection")
  Member "cedr contextualized embeddings for document ranking":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "interpretable time budget constrained contextualization for re ranking")

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)