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 "exact and efficient top k inference for multi target prediction by querying separable linear relational models"):
  Abstract: Many complex multi-target prediction problems that concern large target spaces are characterised by a need for efficient prediction strategies that avoid the computation of predictions for all targets explicitly. Examples of such problems emerge in several subfields of machine learning, such as collaborative filtering, multi-label classification, dyadic prediction and biological network inference. In this article we analyse efficient and exact algorithms for computing the top-$K$ predictions in the above problem settings, using a general class of models that we refer to as separable linear relational models. We show how to use those inference algorithms, which are modifications of well-known information retrieval methods, in a variety of machine learning settings. Furthermore, we study the possibility of scoring items incompletely, while still retaining an exact top-K retrieval. Experimental results in several application domains reveal that the so-called threshold algorithm is very scalable, performing often many orders of magnitude more efficiently than the naive approach.

Includes members:
  Member "fast label embeddings for extremely large output spaces":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "extreme regression for dynamic search advertising")
  Member "lshtc a benchmark for large scale text classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "integrating domain knowledge using hierarchies to improve deep classifiers")
  Member "identification of functionally related enzymes by learning to rank methods":
    also connected through other hyperedges:
      (none)
  Member "a two step learning approach for solving full and almost full cold start problems in dyadic prediction":
    also connected through other hyperedges:
      (none)
  Member "a comparative study of collaborative filtering algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "interaction aware factorization machines for recommender systems")
  Member "efficient regularized least squares algorithms for conditional ranking on relational data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "symmetric and antisymmetric properties of solutions to kernel based machine learning problems")
  Member "optimal aggregation algorithms for middleware":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "finding interesting frames in deep video analytics a top k approach")
  Member "top down induction of clustering trees":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi label learning with deep forest")

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)