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: "approximate search with quantized sparse representations"
  Abstract: This paper tackles the task of storing a large collection of vectors, such as visual descriptors, and of searching in it. To this end, we propose to approximate database vectors by constrained sparse coding, where possible atom weights are restricted to belong to a finite subset. This formulation encompasses, as particular cases, previous state-of-the-art methods such as product or residual quantization. As opposed to traditional sparse coding methods, quantized sparse coding includes memory usage as a design constraint, thereby allowing us to index a large collection such as the BIGANN billion-sized benchmark. Our experiments, carried out on standard benchmarks, show that our formulation leads to competitive solutions when considering different trade-offs between learning/coding time, index size and search quality.

Connected through hyperedge HE-1 (induced by source paper "reconfigurable inverted index"):
  with members:
    - "link and code fast indexing with graphs and compact regression codes"
    - "polysemous codes"
    - "revisiting the inverted indices for billion scale approximate nearest neighbors"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "billion scale similarity search with gpus"
    - "pqk means billion scale clustering for product quantized codes"
    - "accelerated nearest neighbor search with quick adc"
    - "bolt accelerated data mining with fast vector compression"

Connected through hyperedge HE-2 (induced by source paper "revisiting the inverted indices for billion scale approximate nearest neighbors"):
  with members:
    - "polysemous codes"
    - "low shot learning with large scale diffusion"
    - "billion scale similarity search with gpus"
    - "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-3 (induced by source paper "subic a supervised structured binary code for image search"):
  with members:
    - "billion scale similarity search with gpus"
    - "deep image retrieval learning global representations for image search"
    - "how should we evaluate supervised hashing"
    - "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"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"
    - "indexing of cnn features for large scale image search"

Connected through hyperedge HE-4 (induced by source paper "hashing in the zero shot framework with domain adaptation"):
  with members:
    - "a survey on learning to hash"
    - "zero shot hashing"
    - "latent embeddings for zero shot classification"
    - "learning attributes equals multi source domain generalization"
    - "learning to hash for indexing big data a survey"
    - "very deep convolutional networks for large scale image recognition"
    - "matconvnet convolutional neural networks for matlab"
    - "hashing for similarity search a survey"

Connected through hyperedge HE-5 (induced by source paper "learning a complete image indexing pipeline"):
  with members:
    - "subic a supervised structured binary code for image search"
    - "deep image retrieval learning global representations for image search"
    - "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"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"
    - "very deep convolutional networks for large scale image recognition"

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"
    - "learning to hash for indexing big data a survey"
    - "optimized cartesian k means"
    - "going deeper with convolutions"
    - "improving bilayer product quantization for billion scale approximate nearest neighbors in high dimensions"

Connected through hyperedge HE-7 (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"
    - "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"
    - "learning to hash for indexing big data a survey"

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)