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 "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.

Includes members:
  Member "approximate polytope membership queries":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "coresets for the nearest neighbor rule")
  Member "optimal data dependent hashing for approximate near neighbors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "autoblock a hands off blocking framework for entity matching")
  Member "optimal lower bounds for locality sensitive hashing except when q is tiny":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on deep hashing methods")
  Member "fast construction of nets in low dimensional metrics and their applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a data dependent algorithm for querying earth mover s distance with low doubling dimension")
  Member "entropy based nearest neighbor search in high dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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)