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: "efanna an extremely fast approximate nearest neighbor search algorithm based on knn graph"
  Abstract: Approximate nearest neighbor (ANN) search is a fundamental problem in many areas of data mining, machine learning and computer vision. The performance of traditional hierarchical structure (tree) based methods decreases as the dimensionality of data grows, while hashing based methods usually lack efficiency in practice. Recently, the graph based methods have drawn considerable attention. The main idea is that \emph{a neighbor of a neighbor is also likely to be a neighbor}, which we refer as \emph{NN-expansion}. These methods construct a $k$-nearest neighbor ($k$NN) graph offline. And at online search stage, these methods find candidate neighbors of a query point in some way (\eg, random selection), and then check the neighbors of these candidate neighbors for closer ones iteratively. Despite some promising results, there are mainly two problems with these approaches: 1) These approaches tend to converge to local optima. 2) Constructing a $k$NN graph is time consuming. We find that these two problems can be nicely solved when we provide a good initialization for NN-expansion. In this paper, we propose EFANNA, an extremely fast approximate nearest neighbor search algorithm based on $k$NN Graph. Efanna nicely combines the advantages of hierarchical structure based methods and nearest-neighbor-graph based methods. Extensive experiments have shown that EFANNA outperforms the state-of-art algorithms both on approximate nearest neighbor search and approximate nearest neighbor graph construction. To the best of our knowledge, EFANNA is the fastest algorithm so far both on approximate nearest neighbor graph construction and approximate nearest neighbor search. A library EFANNA based on this research is released on Github.

Connected through hyperedge HE-1 (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"
    - "fast approximate nearest neighbor search with navigating spreading out graphs"
    - "sequence to sequence learning as beam search optimization"
    - "generative adversarial imitation learning"
    - "semi supervised classification with graph convolutional networks"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"

Connected through hyperedge HE-2 (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"
    - "fast approximate nearest neighbor search with navigating spreading out graphs"
    - "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 "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"
    - "fast approximate nearest neighbor search with navigating spreading out graphs"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "ggnn graph based gpu nearest neighbor search"):
  with members:
    - "vector and line quantization for billion scale similarity search on gpus"
    - "fast approximate nearest neighbor search with navigating spreading out graphs"
    - "efficient large scale approximate nearest neighbor search on the gpu"
    - "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-5 (induced by source paper "on the merge of k nn graph"):
  with members:
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "fast approximate nearest neighbor search with navigating spreading out graphs"
    - "a survey on learning to hash"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"

Connected through hyperedge HE-6 (induced by source paper "satellite system graph towards the efficiency up boundary of graph based approximate nearest neighbor search"):
  with members:
    - "hd index pushing the scalability accuracy boundary for approximate knn search in high dimensional spaces"
    - "billion scale similarity search with gpus"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"

Connected through hyperedge HE-7 (induced by source paper "graph based nearest neighbor search promises and failures"):
  with members:
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "permutation search methods are efficient yet faster search is possible"

Connected through hyperedge HE-8 (induced by source paper "fabsearch a 3d cad model based search engine for sourcing manufacturing services"):
  with members:
    - "scalable nearest neighbor search based on knn graph"
    - "thingi10k a dataset of 10 000 3d printing models"
    - "octnet learning deep 3d representations at high resolutions"
    - "shapenet an information rich 3d model repository"
    - "multi view convolutional neural networks for 3d shape recognition"

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.CV(Computer Vision and Pattern Recognition)