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: "improving bilayer product quantization for billion scale approximate nearest neighbors in high dimensions"
  Abstract: The top-performing systems for billion-scale high-dimensional approximate nearest neighbor (ANN) search are all based on two-layer architectures that include an indexing structure and a compressed datapoints layer. An indexing structure is crucial as it allows to avoid exhaustive search, while the lossy data compression is needed to fit the dataset into RAM. Several of the most successful systems use product quantization (PQ) for both the indexing and the dataset compression layers. These systems are however limited in the way they exploit the interaction of product quantization processes that happen at different stages of these systems. #R##N#Here we introduce and evaluate two approximate nearest neighbor search systems that both exploit the synergy of product quantization processes in a more efficient way. The first system, called Fast Bilayer Product Quantization (FBPQ), speeds up the runtime of the baseline system (Multi-D-ADC) by several times, while achieving the same accuracy. The second system, Hierarchical Bilayer Product Quantization (HBPQ) provides a significantly better recall for the same runtime at a cost of small memory footprint increase. For the BIGANN dataset of billion SIFT descriptors, the 10% increase in Recall@1 and the 17% increase in Recall@10 is observed.

Connected through hyperedge HE-1 (induced by source paper "vector and line quantization for billion scale similarity search on gpus"):
  with members:
    - "revisiting the inverted indices for billion scale approximate nearest neighbors"
    - "billion scale similarity search with gpus"
    - "efficient large scale approximate nearest neighbor search on the gpu"
    - "deep residual learning for image recognition"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "searching in one billion vectors re rank with source coding"

Connected through hyperedge HE-2 (induced by source paper "link and code fast indexing with graphs and compact regression codes"):
  with members:
    - "polysemous codes"
    - "low shot learning with large scale diffusion"
    - "billion scale similarity search with gpus"
    - "deep image retrieval learning global representations for image search"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "multi scale orderless pooling of deep convolutional activation features"

Connected through hyperedge HE-3 (induced by source paper "polysemous codes"):
  with members:
    - "yfcc100m the new data in multimedia research"
    - "stacked quantizers for compositional vector compression"
    - "hashing for similarity search a survey"
    - "neural codes for image retrieval"
    - "searching in one billion vectors re rank with source coding"

Connected through hyperedge HE-4 (induced by source paper "zoom ssd based vector search for optimizing accuracy latency and memory"):
  with members:
    - "approximate nearest neighbor search in high dimensions"
    - "recurrent binary embedding for gpu enabled exhaustive retrieval from billion scale semantic vectors"

Connected through hyperedge HE-5 (induced by source paper "billion scale similarity search with gpus"):
  with members:
    - "polysemous codes"
    - "efficient large scale approximate nearest neighbor search on the gpu"
    - "deep image retrieval learning global representations for image search"
    - "yfcc100m the new data in multimedia research"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep residual learning for image recognition"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"

Connected through hyperedge HE-6 (induced by source paper "pqtable non exhaustive fast search for product quantized codes using hash tables"):
  with members:
    - "collaborative quantization for cross modal similarity search"
    - "billion scale similarity search with gpus"
    - "efficient large scale approximate nearest neighbor search on the gpu"
    - "scalable image retrieval by sparse product quantization"
    - "approximate search with quantized sparse representations"
    - "learning to hash for indexing big data a survey"
    - "optimized cartesian k means"
    - "going deeper with convolutions"

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)