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: "fast approximate nearest neighbor search with navigating spreading out graphs"
  Abstract: Approximate nearest neighbor search (ANNS) is a fundamental problem in databases and data mining. A scalable ANNS algorithm should be both memory-efficient and fast. Some early graph-based approaches have shown attractive theoretical guarantees on search time complexity, but they all suffer from the problem of high indexing time complexity. Recently, some graph-based methods have been proposed to reduce indexing complexity by approximating the traditional graphs; these methods have achieved revolutionary performance on million-scale datasets. Yet, they still can not scale to billion-node databases. In this paper, to further improve the search-efficiency and scalability of graph-based methods, we start by introducing four aspects: (1) ensuring the connectivity of the graph; (2) lowering the average out-degree of the graph for fast traversal; (3) shortening the search path; and (4) reducing the index size. Then, we propose a novel graph structure called Monotonic Relative Neighborhood Graph (MRNG) which guarantees very low search complexity (close to logarithmic time). To further lower the indexing complexity and make it practical for billion-node ANNS problems, we propose a novel graph structure named Navigating Spreading-out Graph (NSG) by approximating the MRNG. The NSG takes the four aspects into account simultaneously. Extensive experiments show that NSG outperforms all the existing algorithms significantly. In addition, NSG shows superior performance in the E-commercial search scenario of Taobao (Alibaba Group) and has been integrated into their search engine at billion-node scale.

Connected through hyperedge HE-1 (induced by source paper "approximating spectral clustering via sampling a review"):
  with members:
    - "optimized deformed laplacian for spectrum based community detection in sparse heterogeneous graphs"
    - "revisiting the bethe hessian improved community detection in sparse heterogeneous graphs"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "graph reduction by local variation"
    - "scalable spectral clustering using random binning features"
    - "graph reduction with spectral and cut guarantees"
    - "spectrally approximating large graphs with smaller graphs"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"

Connected through hyperedge HE-2 (induced by source paper "learning to route in similarity graphs"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "super convergence very fast training of residual networks using large learning rates"
    - "sequence to sequence learning as beam search optimization"
    - "generative adversarial imitation learning"
    - "efanna an extremely fast approximate nearest neighbor search algorithm based on knn graph"
    - "semi supervised classification with graph convolutional networks"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"

Connected through hyperedge HE-3 (induced by source paper "towards similarity graphs constructed by deep reinforcement learning"):
  with members:
    - "optimization of indexing based on k nearest neighbor graph for proximity search in high dimensional data"
    - "the case for learned index structures"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "graph based time space trade offs for approximate near neighbors"
    - "efanna an extremely fast approximate nearest neighbor search algorithm based on knn graph"
    - "semi supervised classification with graph convolutional networks"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"

Connected through hyperedge HE-4 (induced by source paper "pyramid a general framework for distributed similarity search"):
  with members:
    - "norm ranging lsh for maximum inner product search"
    - "link and code fast indexing with graphs and compact regression codes"
    - "revisiting the inverted indices for billion scale approximate nearest neighbors"
    - "low shot learning with large scale diffusion"
    - "billion scale similarity search with gpus"
    - "a survey on learning to hash"
    - "clustering is efficient for approximate maximum inner product search"
    - "fast neighborhood graph search using cartesian concatenation"

Connected through hyperedge HE-5 (induced by source paper "graph based nearest neighbor search from practice to theory"):
  with members:
    - "graph based nearest neighbor search promises and failures"
    - "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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "relevance proximity graphs for fast relevance retrieval"):
  with members:
    - "catboost unbiased boosting with categorical features"
    - "neural collaborative filtering"
    - "modelling domain relationships for transfer learning on retrieval based question answering systems in e commerce"
    - "super convergence very fast training of residual networks using large learning rates"
    - "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"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "ggnn graph based gpu nearest neighbor search"):
  with members:
    - "vector and line quantization for billion scale similarity search on gpus"
    - "efficient large scale approximate nearest neighbor search on the gpu"
    - "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"
    - "searching in one billion vectors re rank with source coding"

Connected through hyperedge HE-8 (induced by source paper "on the merge of k nn graph"):
  with members:
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "a survey on learning to hash"
    - "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"

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.LG(Machine Learning)