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 "sublinear data structures for short fr echet queries"):
  Abstract: We study metric data structures for curves in doubling spaces, such as trajectories of moving objects in Euclidean $\mathbb{R}^d$, where the distance between two curves is measured using the discrete Frechet distance. We design data structures in an \emph{asymmetric} setting where the input is a curve (or a set of $n$ curves) each of complexity $m$ and the queries are with curves of complexity $k\ll m$. We show that there exist approximate data structures that are independent of the input size $N = d \cdot n \cdot m$ and we study how to maintain them dynamically if the input is given in the stream. #R##N#Concretely, we study two types of data structures: (i) distance oracles, where the task is to store a compressed version of the input curve, which can be used to answer queries for the distance of a query curve to the input curve, and (ii) nearest-neighbor data structures, where the task is to preprocess a set of input curves to answer queries for the input curve closest to the query curve. In both cases we are interested in approximation. For curves embedded in Euclidean $\mathbb{R}^d$ with constant $d$, our distance oracle uses space in $\mathcal{O}((k \log(\epsilon^{-1}) \epsilon^{-d})^k)$ ($\epsilon$ is the precision parameter). The oracle performs $(1+\epsilon)$-approximate queries in time in $\mathcal{O}(k^2)$ and is deterministic. We show how to maintain this distance oracle in the stream using polylogarithmic additional memory. In the stream, we can dynamically answer distance queries to the portion of the stream seen so far in $\mathcal{O}(k^4 \log^2 m)$ time. We apply our techniques to the second problem, approximate near neighbor (ANN) data structures, and achieve an exponential improvement in the dependency on the complexity of the input curves compared to the state of the art.

Includes members:
  Member "the one way communication complexity of dynamic time warping distance":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic time warping in strongly subquadratic time algorithms for the low distance regime and approximate evaluation")
  Member "approximate nearest neighbor for curves simple efficient and deterministic":
    also connected through other hyperedges:
      (none)
  Member "on optimal polyline simplification using the hausdorff and fr echet distance":
    also connected through other hyperedges:
      (none)
  Member "a fast implementation of near neighbors queries for frechet distance gis cup":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "random projections and sampling algorithms for clustering of high dimensional polygonal curves")
  Member "on the complexity of range searching among curves":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximate nearest neighbor for curves simple efficient and deterministic")
  Member "products of euclidean metrics and applications to proximity questions among curves":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dynamic time warping in strongly subquadratic time algorithms for the low distance regime and approximate evaluation")
  Member "coresets and sketches":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "layered sampling for robust optimization problems")
  Member "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "graphs cannot be indexed in polynomial time for sub quadratic time string matching unless seth fails")

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.CG(Computational Geometry)