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 "plug and play dual tree algorithm runtime analysis"):
  Abstract: Numerous machine learning algorithms contain pairwise statistical problems at their core---that is, tasks that require computations over all pairs of input points if implemented naively. Often, tree structures are used to solve these problems efficiently. Dual-tree algorithms can efficiently solve or approximate many of these problems. Using cover trees, rigorous worst-case runtime guarantees have been proven for some of these algorithms. In this paper, we present a problem-independent runtime guarantee for any dual-tree algorithm using the cover tree, separating out the problem-dependent and the problem-independent elements. This allows us to just plug in bounds for the problem-dependent elements to get runtime guarantees for dual-tree algorithms for any pairwise statistical problem without re-deriving the entire proof. We demonstrate this plug-and-play procedure for nearest-neighbor search and approximate kernel density estimation to get improved runtime guarantees. Under mild assumptions, we also present the first linear runtime guarantee for dual-tree based range search.

Includes members:
  Member "tree independent dual tree algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fast approximate furthest neighbors with data dependent hashing")
  Member "the anchors hierachy using the triangle inequality to survive high dimensional data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ball tree efficient spatial indexing for constrained nearest neighbor search in metric spaces")
  Member "variational dual tree framework for large scale transition matrix approximation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the bregman variational dual tree framework")
  Member "mlpack a scalable c machine learning library":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on explaining machine learning models by evolving crucial and compact features")
  Member "faster gaussian summation theory and experiment":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "solvers for mathcal o n electronic structure in the strong scaling limit")
  Member "fast exact max kernel search":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "index based high dimensional cosine threshold querying with optimality guarantees")

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.DS(Data Structures and Algorithms)