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 ell_p spaces 2 p infty via embeddings"
  Abstract: While the problem of approximate nearest neighbor search has been well-studied for Euclidean space and $\ell_1$, few non-trivial algorithms are known for $\ell_p$ when ($2 < p < \infty$). In this paper, we revisit this fundamental problem and present approximate nearest-neighbor search algorithms which give the first non-trivial approximation factor guarantees in this setting.

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"
    - "randomized embeddings with slack and high dimensional approximate nearest neighbor"
    - "fast construction of nets in low dimensional metrics and their applications"

Connected through hyperedge HE-2 (induced by source paper "approximate nearest neighbor search in high dimensions"):
  with members:
    - "optimal las vegas locality sensitive data structures"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "learning to hash for indexing big data a survey"
    - "tight lower bounds for data dependent locality sensitive hashing"
    - "locality sensitive hashing without false negatives"
    - "hashing for similarity search a survey"
    - "sparser johnson lindenstrauss transforms"
    - "optimal lower bounds for locality sensitive hashing except when q is tiny"

Connected through hyperedge HE-3 (induced by source paper "approximate near neighbors for general symmetric norms"):
  with members:
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "learning to hash for indexing big data a survey"
    - "streaming symmetric norms via measure concentration"
    - "optimal data dependent hashing for approximate near neighbors"
    - "sketching and embedding are equivalent for norms"
    - "hashing for similarity search a survey"
    - "a directed isoperimetric inequality with application to bregman near neighbor lower bounds"
    - "a unified framework for linear dimensionality reduction in l1"

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)