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 on high dimensional data experiments analyses and improvement v1 0"
  Abstract: Approximate Nearest neighbor search (ANNS) is fundamental and essential operation in applications from many domains, such as databases, machine learning, multimedia, and computer vision. Although many algorithms have been continuously proposed in the literature in the above domains each year, there is no comprehensive evaluation and analysis of their performances. #R##N#In this paper, we conduct a comprehensive experimental evaluation of many state-of-the-art methods for approximate nearest neighbor search. Our study (1) is cross-disciplinary (i.e., including 16 algorithms in different domains, and from practitioners) and (2) has evaluated a diverse range of settings, including 20 datasets, several evaluation metrics, and different query workloads. The experimental results are carefully reported and analyzed to understand the performance results. Furthermore, we propose a new method that achieves both high query efficiency and high recall empirically on majority of the datasets under a wide range of settings.

Connected through hyperedge HE-1 (induced by source paper "beyond precision a study on recall of initial retrieval with neural representations"):
  with members:
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "a deep relevance matching model for ad hoc retrieval"
    - "a dual embedding space model for document ranking"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "using centroids of word embeddings and word mover s distance for biomedical document retrieval in question answering"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "fast k means based on knn graph"):
  with members:
    - "boost k means"
    - "efanna an extremely fast approximate nearest neighbor search algorithm based on knn graph"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "yfcc100m the new data in multimedia research"
    - "fast approximate k means via cluster closures"
    - "scalable k means"
    - "k means requires exponentially many iterations even in the plane"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-3 (induced by source paper "fast approximate nearest neighbor search with navigating spreading out graphs"):
  with members:
    - "billion scale similarity search with gpus"
    - "efanna an extremely fast approximate nearest neighbor search algorithm based on knn graph"
    - "a revisit of hashing algorithms for approximate nearest neighbor search"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "approximate similarity search for online multimedia services on distributed cpu gpu platforms"

Connected through hyperedge HE-4 (induced by source paper "a revisit of hashing algorithms for approximate nearest neighbor search"):
  with members:
    - "fast approximate nearest neighbor search with navigating spreading out graphs"
    - "efanna an extremely fast approximate nearest neighbor search algorithm based on knn graph"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "hashing for similarity search a survey"
    - "self taught hashing for fast similarity search"

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.DB(Databases)