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 "lower bounds on performance of metric tree indexing schemes for exact similarity search in high dimensions"):
  Abstract: Within a mathematically rigorous model, we analyse the curse of dimensionality for deterministic exact similarity search in the context of popular indexing schemes: metric trees. The datasets $X$ are sampled randomly from a domain $\Omega$, equipped with a distance, $\rho$, and an underlying probability distribution, $\mu$. While performing an asymptotic analysis, we send the intrinsic dimension $d$ of $\Omega$ to infinity, and assume that the size of a dataset, $n$, grows superpolynomially yet subexponentially in $d$. Exact similarity search refers to finding the nearest neighbour in the dataset $X$ to a query point $\omega\in\Omega$, where the query points are subject to the same probability distribution $\mu$ as datapoints. Let $\mathscr F$ denote a class of all 1-Lipschitz functions on $\Omega$ that can be used as decision functions in constructing a hierarchical metric tree indexing scheme. Suppose the VC dimension of the class of all sets $\{\omega\colon f(\omega)\geq a\}$, $a\in\R$ is $o(n^{1/4}/\log^2n)$. (In view of a 1995 result of Goldberg and Jerrum, even a stronger complexity assumption $d^{O(1)}$ is reasonable.) We deduce the $\Omega(n^{1/4})$ lower bound on the expected average case performance of hierarchical metric-tree based indexing schemes for exact similarity search in $(\Omega,X)$. In paricular, this bound is superpolynomial in $d$.

Includes members:
  Member "indexability concentration and vc theory":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "permutation search methods are efficient yet faster search is possible")
  Member "lower bounds on near neighbor search via metric expansion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "tight static lower bounds for non adaptive data structures")
  Member "curse of dimensionality in pivot based indexes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gpu accelerated similarity self join for multi dimensional data")
  Member "intrinsic dimension of a dataset what properties does one expect":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "intrinsic dimension of concept lattices")
  Member "an axiomatic approach to intrinsic dimension of a dataset":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "intrinsic dimension of concept lattices")
  Member "indexing schemes for similarity search in datasets of short protein fragments":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "indexability concentration and vc theory")
  Member "indexing schemes for similarity search an illustrated paradigm":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the i o complexity of the k nearest neighbor problem")
  Member "on the geometry of similarity search dimensionality curse and concentration of measure":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "universal bayes consistency in metric spaces")

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)