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: "local search methods for fast near neighbor search"
  Abstract: Near neighbor search is a powerful abstraction for data access; it allows searching for objects similar to a query. Search indexes are data structures designed to accelerate computing-intensive data processing, like those routinely found in clustering and classification tasks. However, for intrinsically high-dimensional data, competitive indexes tend to have either impractical index construction times or memory usage. A recent turn around in the literature has been introduced with the use of the approximate proximity graph (APG): a connected graph with a greedy search algorithm with restarts, needing sublinear time to solve queries. The APG computes an approximation of the result set using a small memory footprint, i.e., proportional to the underlying graph's degree. The degree along with the number of search repeats determine the speed and accuracy of the algorithm. #R##N#This manuscript introduces three new algorithms based on local-search metaheuristics for the search graph. Two of these algorithms are direct improvements of the original one, yet we reduce the number of free parameters of the algorithm; the third one is an entirely new method that improves both the search speed and the accuracy of the result in most of our benchmarks. We also provide a broad experimental study to characterize our search structures and prove our claims; we also report an extensive performance comparison with the current alternatives.

Connected through hyperedge HE-1 (induced by source paper "accurate and fast retrieval for complex non metric data via neighborhood graphs"):
  with members:
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "permutation search methods are efficient yet faster search is possible"
    - "hashing for similarity search a survey"
    - "a survey on metric learning for feature vectors and structured data"

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.DS(Data Structures and Algorithms)