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: "graph based nearest neighbor search promises and failures"
  Abstract: Recently, graph based nearest neighbor search gets more and more popular on large-scale retrieval tasks. The attractiveness of this type of approaches lies in its superior performance over most of the known nearest neighbor search approaches as well as its genericness to various distance measures. In this paper, the role of two strategies, namely hierarchical structure and graph diversification that are adopted as the key steps in graph based approaches, is investigated. We find that the hierarchical structure could not achieve "a much better logarithmic complexity scaling" as it was claimed in the original paper, particularly on high dimensional cases. Moreover, we find that similar high search speed efficiency as the one with hierarchical structure could be achieved with the support of flat k-NN graph after graph diversification. Finally, we point out the difficulty, that is faced by most of the graph based search approaches, is directly linked to "curse of dimensionality".

Connected through hyperedge HE-1 (induced by source paper "graph based nearest neighbor search from practice to theory"):
  with members:
    - "optimization of indexing based on k nearest neighbor graph for proximity search in high dimensional data"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "graph based time space trade offs for approximate near neighbors"
    - "fast approximate nearest neighbor search with navigating spreading out graphs"
    - "tight lower bounds for data dependent locality sensitive hashing"
    - "optimal data dependent hashing for approximate near neighbors"
    - "optimal lower bounds for locality sensitive hashing except when q is tiny"
    - "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.IR(Information Retrieval)