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 "learning groupwise multivariate scoring functions using deep neural networks"):
  Abstract: While in a classification or a regression setting a label or a value is assigned to each individual document, in a ranking setting we determine the relevance ordering of the entire input document list. This difference leads to the notion of relative relevance between documents in ranking. The majority of the existing learning-to-rank algorithms model such relativity at the loss level using pairwise or listwise loss functions. However, they are restricted to univariate scoring functions, i.e., the relevance score of a document is computed based on the document itself, regardless of other documents in the list. To overcome this limitation, we propose a new framework for multivariate scoring functions, in which the relevance score of a document is determined jointly by multiple documents in the list. We refer to this framework as GSFs -- groupwise scoring functions. We learn GSFs with a deep neural network architecture, and demonstrate that several representative learning-to-rank algorithms can be modeled as special cases in our framework. We conduct evaluation using click logs from one of the largest commercial email search engines, as well as a public benchmark dataset. In both cases, GSFs lead to significant performance improvements, especially in the presence of sparse textual features.

Includes members:
  Member "tf ranking scalable tensorflow library for learning to rank":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "separate and attend in personal email search")
  Member "learning a deep listwise context model for ranking refinement":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "listwise learning to rank by exploring unique ratings")
  Member "deeprank a new deep architecture for relevance ranking in information retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "selective weak supervision for neural information retrieval")
  Member "neural ranking models with weak supervision":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "selective weak supervision for neural information retrieval")
  Member "unbiased learning to rank with biased feedback":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "safe exploration for optimizing contextual bandits")
  Member "a deep relevance matching model for ad hoc retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "iart intent aware response ranking with transformers in information seeking conversation systems")
  Member "learning to match using local and distributed representations of text for web search":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "iart intent aware response ranking with transformers in information seeking conversation systems")
  Member "introducing letor 4 0 datasets":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "listwise learning to rank by exploring unique ratings")

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)