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 "batched point location in sinr diagrams via algebraic tools"):
  Abstract: The SINR model for wireless networks has been extensively studied recently. It tries to model whether a particular transmitter is heard at a specific location, with $n$ transmitting simultaneously. The SINR diagram consists of $n$ regions where each transmitter can be heard and the remaining space where no one can be heard. #R##N#Efficient point location in the SINR diagram, i.e., building a data structure to determine, for a query point, whether any transmitter is heard there, and if so, which one, has been recently investigated. Previous such planar data structures are constructed in time at least quadratic in $n$ and support logarithmic-time approximate queries. Moreover, the performance of these depends not only on the number $n$ of transmitters and on the approximation parameter $\varepsilon$, but also on some geometric parameters that cannot be bounded a priori as a function of $n$ or $\varepsilon$. #R##N#We address the question of batched point location queries, i.e., answering many queries simultaneously. Specifically, in one dimension, we can answer $n$ queries exactly in amortized polylogarithmic time per query, while in the plane we can do it approximately. #R##N#All these results can handle arbitrary power assignments to the transmitters. Moreover, the amortized query time in these results depends only on $n$ and $\varepsilon$. #R##N#We also show how to speed up the preprocessing in a previously proposed point-location structure in SINR diagram for uniform-power sites, by almost a full order of magnitude. For this we obtain results on the sensitivity of the reception regions to slight changes in the reception threshold, which are of independent interest. #R##N#Finally, these results demonstrate the (so far underutilized) power of combining algebraic tools with those of computational geometry and other fields.

Includes members:
  Member "approximating minimization diagrams and generalized proximity search":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "resolving sinr queries in a dynamic setting")
  Member "sinr diagram with interference cancellation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "resolving sinr queries in a dynamic setting")
  Member "computing the distance between piecewise linear bivariate functions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "computing the inverse geodesic length in planar graphs and graphs of bounded treewidth")
  Member "the topology of wireless communication":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "resolving sinr queries in a dynamic setting")
  Member "wireless capacity with oblivious power in general metrics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reception capacity definitions game theory and hardness")
  Member "a constant factor approximation for wireless capacity maximization with power control in the sinr model":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reception capacity definitions game theory and hardness")
  Member "wireless scheduling with power control":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "effective wireless scheduling via hypergraph sketches")
  Member "fast multipoint evaluation of bivariate polynomials":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fast systematic encoding of multiplicity codes")

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)