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: "randomized embeddings with slack and high dimensional approximate nearest neighbor"
  Abstract: The approximate nearest neighbor problem ($\epsilon$-ANN) in high dimensional Euclidean space has been mainly addressed by Locality Sensitive Hashing (LSH), which has polynomial dependence in the dimension, sublinear query time, but subquadratic space requirement. In this paper, we introduce a new definition of "low-quality" embeddings for metric spaces. It requires that, for some query point $q$, there exists an approximate nearest neighbor among the pre-images of the $k>1$ approximate nearest neighbors in the target space. Focusing on Euclidean spaces, we employ random projections in order to reduce the original problem to one in a space of dimension inversely proportional to $k$. #R##N#The $k$ approximate nearest neighbors can be efficiently retrieved by a data structure such as BBD-trees. The same approach is applied to the problem of computing an approximate near neighbor, where we obtain a data structure requiring linear space, and query time in $O(d n^{\rho})$, for $\rho\approx 1-\epsilon^2/\log(1/\epsilon)$. This directly implies a solution for $\epsilon$-ANN, while achieving a better exponent in the query time than the method based on BBD-trees. Better bounds are obtained in the case of doubling subsets of $\ell_2$, by combining our method with $r$-nets. #R##N#We implement our method in C++, and present experimental results in dimension up to $500$ and $10^6$ points, which show that performance is better than predicted by the analysis. In addition, we compare our ANN approach to E2LSH, which implements LSH, and we show that the theoretical advantages of each method are reflected on their actual performance.

Connected through hyperedge HE-1 (induced by source paper "near neighbor preserving dimension reduction for doubling subsets of ell_1"):
  with members:
    - "approximate near neighbors for general symmetric norms"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "approximate greedy clustering and distance selection for graph metrics"
    - "approximate nearest neighbor search for ell_p spaces 2 p infty via embeddings"
    - "fast construction of nets in low dimensional metrics and their applications"

Connected through hyperedge HE-2 (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"
    - "low quality dimension reduction 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"

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)