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: "stacked quantizers for compositional vector compression"
  Abstract: Recently, Babenko and Lempitsky introduced Additive Quantization (AQ), a generalization of Product Quantization (PQ) where a non-independent set of codebooks is used to compress vectors into small binary codes. Unfortunately, under this scheme encoding cannot be done independently in each codebook, and optimal encoding is an NP-hard problem. In this paper, we observe that PQ and AQ are both compositional quantizers that lie on the extremes of the codebook dependence-independence assumption, and explore an intermediate approach that exploits a hierarchical structure in the codebooks. This results in a method that achieves quantization error on par with or lower than AQ, while being several orders of magnitude faster. We perform a complexity analysis of PQ, AQ and our method, and evaluate our approach on standard benchmarks of SIFT and GIST descriptors, as well as on new datasets of features obtained from state-of-the-art convolutional neural networks.

Connected through hyperedge HE-1 (induced by source paper "asymmetric correlation quantization hashing for cross modal retrieval"):
  with members:
    - "fast supervised discrete hashing"
    - "collaborative quantization for cross modal similarity search"
    - "deep binary reconstruction for cross modal hashing"
    - "composite correlation quantization for efficient multimodal retrieval"
    - "deep cross modal hashing"
    - "multimodal similarity preserving hashing"

Connected through hyperedge HE-2 (induced by source paper "deep recurrent quantization for generating sequential binary codes"):
  with members:
    - "deep triplet quantization"
    - "deep supervised discrete hashing"
    - "deep supervised hashing with triplet labels"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "fast supervised hashing with decision trees for high dimensional data"

Connected through hyperedge HE-3 (induced by source paper "efficient inner product approximation in hybrid spaces"):
  with members:
    - "deep cross network for ad click predictions"
    - "accelerated nearest neighbor search with quick adc"
    - "bolt accelerated data mining with fast vector compression"
    - "attentional factorization machines learning the weight of feature interactions via attention networks"
    - "practical and optimal lsh for angular distance"
    - "quantization based fast inner product search"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-4 (induced by source paper "beyond product quantization deep progressive quantization for image retrieval"):
  with members:
    - "in defense of product quantization"
    - "a survey on learning to hash"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "netvlad cnn architecture for weakly supervised place recognition"

Connected through hyperedge HE-5 (induced by source paper "local orthogonal decomposition for maximum inner product search"):
  with members:
    - "billion scale similarity search with gpus"
    - "learning spread out local feature descriptors"
    - "bolt accelerated data mining with fast vector compression"
    - "a greedy approach for budgeted maximum inner product search"

Connected through hyperedge HE-6 (induced by source paper "polysemous codes"):
  with members:
    - "yfcc100m the new data in multimedia research"
    - "hashing for similarity search a survey"
    - "neural codes for image retrieval"
    - "improving bilayer product quantization for billion scale approximate nearest neighbors in high dimensions"
    - "searching in one billion vectors re rank with source coding"

Connected through hyperedge HE-7 (induced by source paper "approximate k nn graph construction a generic online approach"):
  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"
    - "query adaptive image retrieval by deep weighted hashing"
    - "fast online k nn graph building"

Connected through hyperedge HE-8 (induced by source paper "bolt accelerated data mining with fast vector compression"):
  with members:
    - "polysemous codes"
    - "structured adaptive and random spinners for fast machine learning computations"
    - "orthogonal random features"
    - "loss aware binarization of deep networks"
    - "generalized residual vector quantization for large scale data"
    - "learning both weights and connections for efficient neural networks"
    - "acdc a structured efficient linear layer"
    - "practical and optimal lsh for angular distance"

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)