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 "pigeonring a principle for faster thresholded similarity search"):
  Abstract: The pigeonhole principle states that if n items are contained in m boxes, then at least one box has no fewer than n/m items. It is utilized to solve many data management problems, especially for thresholded similarity searches. Despite many pigeonhole principle-based solutions proposed in the last few decades, the condition stated by the principle is weak. It only constrains the number of items in a single box. By organizing the boxes in a ring, we observe that the number of items in multiple boxes are also constrained. We propose a new principle called the pigeonring principle which formally captures such constraints and yields stronger conditions. To utilize the pigeonring principle, we focus on problems defined in the form of identifying data objects whose similarities or distances to the query is constrained by a threshold. Many solutions to these problems utilize the pigeonhole principle to find candidates that satisfy a filtering condition. By the pigeonring principle, stronger filtering conditions can be established. We show that the pigeonhole principle is a special case of the pigeonring principle. This suggests that all the solutions based on the pigeonhole principle are possible to be accelerated by the pigeonring principle. A universal filtering framework is introduced to encompass the solutions to these problems based on the pigeonring principle. Besides, we discuss how to quickly find candidates specified by the pigeonring principle with minor modifications on top of existing algorithms. Experimental results on real datasets demonstrate the applicability of the pigeonring principle as well as the superior performance of the algorithms based on the principle.

Includes members:
  Member "scalable and robust set similarity join":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "blocking and filtering techniques for entity resolution a survey")
  Member "set similarity search beyond minhash":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "blocking and filtering techniques for entity resolution a survey")
  Member "practical and optimal lsh for angular distance":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "differentiable reasoning over a virtual knowledge base")
  Member "hashing for similarity search a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on deep hashing methods")
  Member "alae accelerating local alignment with affine gap exactly in biosequence databases":
    also connected through other hyperedges:
      (none)
  Member "searching in one billion vectors re rank with source coding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to compress and search visual data in large scale systems")
  Member "pass join a partition based method for similarity joins":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "blocking and filtering techniques for entity resolution a survey")
  Member "bayesian locality sensitive hashing for fast similarity search":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "blocking and filtering techniques for entity resolution a survey")

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.DB(Databases)