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 "approximate k flat nearest neighbor search"):
  Abstract: Let $k$ be a nonnegative integer. In the approximate $k$-flat nearest neighbor ($k$-ANN) problem, we are given a set $P \subset \mathbb{R}^d$ of $n$ points in $d$-dimensional space and a fixed approximation factor $c > 1$. Our goal is to preprocess $P$ so that we can efficiently answer approximate $k$-flat nearest neighbor queries: given a $k$-flat $F$, find a point in $P$ whose distance to $F$ is within a factor $c$ of the distance between $F$ and the closest point in $P$. The case $k = 0$ corresponds to the well-studied approximate nearest neighbor problem, for which a plethora of results are known, both in low and high dimensions. The case $k = 1$ is called approximate line nearest neighbor. In this case, we are aware of only one provably efficient data structure, due to Andoni, Indyk, Krauthgamer, and Nguyen. For $k \geq 2$, we know of no previous results. #R##N#We present the first efficient data structure that can handle approximate nearest neighbor queries for arbitrary $k$. We use a data structure for $0$-ANN-queries as a black box, and the performance depends on the parameters of the $0$-ANN solution: suppose we have an $0$-ANN structure with query time $O(n^{\rho})$ and space requirement $O(n^{1+\sigma})$, for $\rho, \sigma > 0$. Then we can answer $k$-ANN queries in time $O(n^{k/(k + 1 - \rho) + t})$ and space $O(n^{1+\sigma k/(k + 1 - \rho)} + n\log^{O(1/t)} n)$. Here, $t > 0$ is an arbitrary constant and the $O$-notation hides exponential factors in $k$, $1/t$, and $c$ and polynomials in $d$. Our new data structures also give an improvement in the space requirement over the previous result for $1$-ANN: we can achieve near-linear space and sublinear query time, a further step towards practical applications where space constitutes the bottleneck.

Includes members:
  Member "beyond locality sensitive hashing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic streaming spectral sparsification in nearly linear time and space")
  Member "entropy based nearest neighbor search in high dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "algorithms for similarity search and pseudorandomness")

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)