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 nearest neighbor search for low dimensional queries"
  Abstract: We study the Approximate Nearest Neighbor problem for metric spaces where the query points are constrained to lie on a subspace of low doubling dimension, while the data is high-dimensional. We show that this problem can be solved efficiently despite the high dimensionality of the data.

Connected through hyperedge HE-1 (induced by source paper "comparison based learning from weak oracles"):
  with members:
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "learning combinatorial functions from pairwise comparisons"
    - "leveraging transitive relations for crowdsourced joins"
    - "crowder crowdsourcing entity resolution"
    - "comparison based learning with rank nets"
    - "adaptively learning the crowd kernel"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "hierarchical routing over dynamic wireless networks"

Connected through hyperedge HE-2 (induced by source paper "binary coding in stream"):
  with members:
    - "spectral approaches to nearest neighbor search"
    - "relative errors for deterministic low rank matrix approximations"
    - "the supervised ibp neighbourhood preserving infinite latent feature models"
    - "simple and deterministic matrix sketching"

Connected through hyperedge HE-3 (induced by source paper "spectral approaches to nearest neighbor search"):
  with members:
    - "beyond locality sensitive hashing"
    - "settling the polynomial learnability of mixtures of gaussians"
    - "fast construction of nets in low dimensional metrics and their applications"
    - "entropy based nearest neighbor search in high dimensions"

Connected through hyperedge HE-4 (induced by source paper "down the rabbit hole robust proximity search and density estimation in sublinear space"):
  with members:
    - "lower bounds for k distance approximation"
    - "witnessed k distance"

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)