human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "on the complexity of range searching among curves"
  Abstract: Modern tracking technology has made the collection of large numbers of densely sampled trajectories of moving objects widely available. We consider a fundamental problem encountered when analysing such data: Given $n$ polygonal curves $S$ in $\mathbb{R}^d$, preprocess $S$ into a data structure that answers queries with a query curve $q$ and radius $\rho$ for the curves of $S$ that have \Frechet distance at most $\rho$ to $q$. #R##N#We initiate a comprehensive analysis of the space/query-time trade-off for this data structuring problem. Our lower bounds imply that any data structure in the pointer model model that achieves $Q(n) + O(k)$ query time, where $k$ is the output size, has to use roughly $\Omega\left((n/Q(n))^2\right)$ space in the worst case, even if queries are mere points (for the discrete \Frechet distance) or line segments (for the continuous \Frechet distance). More importantly, we show that more complex queries and input curves lead to additional logarithmic factors in the lower bound. Roughly speaking, the number of logarithmic factors added is linear in the number of edges added to the query and input curve complexity. This means that the space/query time trade-off worsens by an exponential factor of input and query complexity. This behaviour addresses an open question in the range searching literature: whether it is possible to avoid the additional logarithmic factors in the space and query time of a multilevel partition tree. We answer this question negatively. #R##N#On the positive side, we show we can build data structures for the \Frechet distance by using semialgebraic range searching. Our solution for the discrete \Frechet distance is in line with the lower bound, as the number of levels in the data structure is $O(t)$, where $t$ denotes the maximal number of vertices of a curve. For the continuous \Frechet distance, the number of levels increases to $O(t^2)$.

Connected through hyperedge HE-1 (induced by source paper "sublinear data structures for short fr echet queries"):
  with members:
    - "the one way communication complexity of dynamic time warping distance"
    - "approximate nearest neighbor for curves simple efficient and deterministic"
    - "on optimal polyline simplification using the hausdorff and fr echet distance"
    - "a fast implementation of near neighbors queries for frechet distance gis cup"
    - "products of euclidean metrics and applications to proximity questions among curves"
    - "coresets and sketches"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"
    - "fast construction of nets in low dimensional metrics and their applications"

Connected through hyperedge HE-2 (induced by source paper "approximate nearest neighbor for curves simple efficient and deterministic"):
  with members:
    - "sublinear data structures for short fr echet queries"
    - "nonlinear dimension reduction via outer bi lipschitz extensions"
    - "optimal terminal dimensionality reduction in euclidean space"
    - "locality sensitive hashing of curves"
    - "products of euclidean metrics and applications to proximity questions among curves"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"
    - "faster retrieval with a two pass dynamic time warping lower bound"

Connected through hyperedge HE-3 (induced by source paper "efficient nearest neighbor query and clustering of planar curves"):
  with members:
    - "locality sensitive hashing of curves"
    - "products of euclidean metrics and applications to proximity questions among curves"
    - "spatio temporal analysis of team sports"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"
    - "computing the discrete fr echet distance in subquadratic time"

Connected through hyperedge HE-4 (induced by source paper "the vc dimension of metric balls under fr echet and hausdorff distances"):
  with members:
    - "a fast implementation of near neighbors queries for frechet distance gis cup"
    - "products of euclidean metrics and applications to proximity questions among curves"
    - "spatio temporal analysis of team sports"
    - "a new upper bound for the vc dimension of visibility regions"

Connected through hyperedge HE-5 (induced by source paper "fresh fr echet similarity with hashing"):
  with members:
    - "approximate nearest neighbor search in high dimensions"
    - "a fast implementation of near neighbors queries for frechet distance gis cup"
    - "locality sensitive hashing of curves"
    - "products of euclidean metrics and applications to proximity questions among curves"
    - "fast locality sensitive hashing for approximate near neighbor search"
    - "fast locality sensitive hashing frameworks for approximate near neighbor search"
    - "spatio temporal analysis of team sports"
    - "parameter free locality sensitive hashing for spherical range reporting"

Connected through hyperedge HE-6 (induced by source paper "probabilistic embeddings of the fr echet distance"):
  with members:
    - "products of euclidean metrics and applications to proximity questions among curves"
    - "dynamic time warping and geometric edit distance breaking the quadratic barrier"
    - "quadratic conditional lower bounds for string problems and dynamic time warping"
    - "approximating dynamic time warping and edit distance for a pair of point sequences"
    - "quadratic time hardness of lcs and other sequence similarity measures"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"
    - "on the impossibility of dimension reduction for doubling subsets of ell_p p 2"
    - "four soviets walk the dog with an application to alt s conjecture"

Question: Please tell me which class the center node 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)