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: "low quality dimension reduction and high dimensional approximate nearest neighbor"
  Abstract: The approximate nearest neighbor problem ($\epsilon$-ANN) in Euclidean settings is a fundamental question, which has been addressed by two main approaches: Data-dependent space partitioning techniques perform well when the dimension is relatively low, but are affected by the curse of dimensionality. On the other hand, locality sensitive hashing has polynomial dependence in the dimension, sublinear query time with an exponent inversely proportional to the error factor $\epsilon$, and subquadratic space requirement. #R##N#We generalize the Johnson-Lindenstrauss lemma to define "low-quality" mappings to a Euclidean space of significantly lower dimension, such that they satisfy a requirement weaker than approximately preserving all distances or even preserving the nearest neighbor. This mapping guarantees, with arbitrarily high probability, that an approximate nearest neighbor lies among the $k$ approximate nearest neighbors in the projected space. This leads to a randomized tree based data structure that avoids the curse of dimensionality for $\epsilon$-ANN. Our algorithm, given $n$ points in dimension $d$, achieves space usage in $O(dn)$, preprocessing time in $O(dn\log n)$, and query time in $O(d n^{\rho}\log n)$, where $\rho$ is proportional to $1-{1}/{\ln \ln n}$, for fixed $\epsilon \in (0,1)$. It employs a data structure, such as BBD-trees, that efficiently finds $k$ approximate nearest neighbors. The dimension reduction is larger if one assumes that pointsets possess some structure, namely bounded expansion rate. We implement our method and present experimental results in up to 500 dimensions and $10^5$ points, which show that the practical performance is better than predicted by the theoretical analysis. In addition, we compare our approach with E2LSH.

Connected through hyperedge HE-1 (induced by source paper "approximating spectral clustering via sampling a review"):
  with members:
    - "optimized deformed laplacian for spectrum based community detection in sparse heterogeneous graphs"
    - "revisiting the bethe hessian improved community detection in sparse heterogeneous graphs"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "graph reduction by local variation"
    - "scalable spectral clustering using random binning features"
    - "graph reduction with spectral and cut guarantees"
    - "spectrally approximating large graphs with smaller graphs"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"

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

Connected through hyperedge HE-3 (induced by source paper "fast k nearest neighbour search via prioritized dci"):
  with members:
    - "fast k nearest neighbour search via dynamic continuous indexing"
    - "optimal data dependent hashing for approximate near neighbors"

Connected through hyperedge HE-4 (induced by source paper "practical linear space approximate near neighbors in high dimension"):
  with members:
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "practical and optimal lsh for angular distance"
    - "optimal data dependent hashing for approximate near neighbors"
    - "randomized embeddings with slack and high dimensional approximate nearest neighbor"
    - "optimal lower bounds for locality sensitive hashing except when q is tiny"
    - "entropy based nearest neighbor search in high dimensions"
    - "lower bounds on locality sensitive hashing"

Connected through hyperedge HE-5 (induced by source paper "high dimensional approximate nearest neighbor k d generalized randomized forests"):
  with members:
    - "hashing for similarity search a survey"

Connected through hyperedge HE-6 (induced by source paper "randomized embeddings with slack and high dimensional approximate nearest neighbor"):
  with members:
    - "approximate polytope membership queries"
    - "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"
    - "optimal lower bounds for locality sensitive hashing except when q is tiny"
    - "a nonlinear approach to dimension reduction"

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)