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 neighborhood graph search using cartesian concatenation"
  Abstract: In this paper, we propose a new data structure for approximate nearest neighbor search. This structure augments the neighborhood graph with a bridge graph. We propose to exploit Cartesian concatenation to produce a large set of vectors, called bridge vectors, from several small sets of subvectors. Each bridge vector is connected with a few reference vectors near to it, forming a bridge graph. Our approach finds nearest neighbors by simultaneously traversing the neighborhood graph and the bridge graph in the best-first strategy. The success of our approach stems from two factors: the exact nearest neighbor search over a large number of bridge vectors can be done quickly, and the reference vectors connected to a bridge (reference) vector near the query are also likely to be near the query. Experimental results on searching over large scale datasets (SIFT, GIST and HOG) show that our approach outperforms state-of-the-art ANN search algorithms in terms of efficiency and accuracy. The combination of our approach with the IVFADC system also shows superior performance over the BIGANN dataset of $1$ billion SIFT features compared with the best previously published result.

Connected through hyperedge HE-1 (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"
    - "fast approximate nearest neighbor search with navigating spreading out graphs"
    - "a survey on learning to hash"
    - "clustering is efficient for approximate maximum inner product search"

Connected through hyperedge HE-2 (induced by source paper "supervised quantization for similarity search"):
  with members:
    - "hashing on nonlinear manifolds"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "hashing with binary autoencoders"
    - "optimized cartesian k means"
    - "hashing for similarity search a survey"
    - "revisiting kernelized locality sensitive hashing for improved large scale image retrieval"
    - "inductive hashing on manifolds"
    - "a general two step approach to learning based hashing"

Connected through hyperedge HE-3 (induced by source paper "understanding and improving proximity graph based maximum inner product search"):
  with members:
    - "norm ranging lsh for maximum inner product search"
    - "to index or not to index optimizing exact maximum inner product search"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "a greedy approach for budgeted maximum inner product search"
    - "quantization based fast inner product search"
    - "distributed representations of words and phrases and their compositionality"
    - "nomad non locking stochastic multi machine algorithm for asynchronous and decentralized matrix completion"

Connected through hyperedge HE-4 (induced by source paper "collaborative quantization for cross modal similarity search"):
  with members:
    - "compositional correlation quantization for large scale multimodal search"
    - "optimized cartesian k means"
    - "hashing for similarity search a survey"
    - "multimodal similarity preserving hashing"

Connected through hyperedge HE-5 (induced by source paper "hd index pushing the scalability accuracy boundary for approximate knn search in high dimensional spaces"):
  with members:
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"

Connected through hyperedge HE-6 (induced by source paper "a survey on learning to hash"):
  with members:
    - "collaborative quantization for cross modal similarity search"
    - "scalable discrete supervised hash learning with asymmetric matrix factorization"
    - "approximate search with quantized sparse representations"
    - "binary hashing with semidefinite relaxation and augmented lagrangian"
    - "learning to hash with binary deep neural network"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "hashing with binary autoencoders"

Connected through hyperedge HE-7 (induced by source paper "off the beaten path let s replace term based retrieval with k nn search"):
  with members:
    - "using centroids of word embeddings and word mover s distance for biomedical document retrieval in question answering"
    - "practical and optimal lsh for angular distance"
    - "permutation search methods are efficient yet faster search is possible"
    - "towards universal paraphrastic sentence embeddings"
    - "distributed representations of sentences and documents"
    - "retrofitting word vectors to semantic lexicons"
    - "hashing for similarity search a survey"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"):
  with members:
    - "polysemous codes"
    - "efficient large scale approximate nearest neighbor search on the gpu"
    - "practical and optimal lsh for angular distance"
    - "permutation search methods are efficient yet faster search is possible"
    - "optimal data dependent hashing for approximate near neighbors"
    - "imagenet large scale visual recognition challenge"
    - "fast exact search in hamming space with multi index hashing"
    - "from small world networks to comparison based 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.CV(Computer Vision and Pattern Recognition)