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: "approximate polytope membership queries"
  Abstract: In the polytope membership problem, a convex polytope $K$ in $\mathbb{R}^d$ is given, and the objective is to preprocess $K$ into a data structure so that, given any query point $q \in \mathbb{R}^d$, it is possible to determine efficiently whether $q \in K$. We consider this problem in an approximate setting. Given an approximation parameter $\varepsilon$, the query can be answered either way if the distance from $q$ to $K$'s boundary is at most $\varepsilon$ times $K$'s diameter. We assume that the dimension $d$ is fixed, and $K$ is presented as the intersection of $n$ halfspaces. Previous solutions to approximate polytope membership were based on straightforward applications of classic polytope approximation techniques by Dudley [Approx. Theory, 10 (1974), pp. 227--236] and Bentley, Faust, and Preparata [Commun. ACM, 25 (1982), pp. 64--68]. The former is optimal in the worst case with respect to space, and the latter is optimal with respect to query time. We present four main results. First, we show how...

Connected through hyperedge HE-1 (induced by source paper "coresets for the nearest neighbor rule"):
  with members:
    - "guarantees on nearest neighbor condensation heuristics"
    - "on the minimum consistent subset problem"

Connected through hyperedge HE-2 (induced by source paper "algorithms for deciding membership in polytopes of general dimension"):
  with members:
    - "optimal approximate polytope membership"
    - "practical and optimal lsh for angular distance"
    - "optimal data dependent hashing for approximate near neighbors"
    - "low quality dimension reduction and high dimensional approximate nearest neighbor"
    - "efficient random walk methods for approximating polytope volume"

Connected through hyperedge HE-3 (induced by source paper "optimal approximate polytope membership"):
  with members:
    - "on the combinatorial complexity of approximating polytopes"
    - "optimal detection of intersections between convex polyhedra"
    - "separation sensitive collision detection for convex objects"

Connected through hyperedge HE-4 (induced by source paper "near optimal epsilon kernel construction and related problems"):
  with members:
    - "optimal approximate polytope membership"
    - "on the combinatorial complexity of approximating polytopes"

Connected through hyperedge HE-5 (induced by source paper "randomized embeddings with slack and high dimensional approximate nearest neighbor"):
  with members:
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "high dimensional approximate r nets"
    - "approximate greedy clustering and distance selection for graph metrics"
    - "practical and optimal lsh for angular distance"
    - "optimal data dependent hashing for approximate near neighbors"
    - "low quality dimension reduction and high dimensional approximate nearest neighbor"
    - "optimal lower bounds for locality sensitive hashing except when q is tiny"
    - "a nonlinear approach to dimension reduction"

Connected through hyperedge HE-6 (induced by source paper "low quality dimension reduction and high dimensional approximate nearest neighbor"):
  with members:
    - "optimal data dependent hashing for approximate near neighbors"
    - "optimal lower bounds for locality sensitive hashing except when q is tiny"
    - "fast construction of nets in low dimensional metrics and their applications"
    - "entropy based nearest neighbor search in high dimensions"

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)