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: "polysemous codes"
  Abstract: This paper considers the problem of approximate nearest neighbor search in the compressed domain. We introduce polysemous codes, which offer both the distance estimation quality of product quantization and the efficient comparison of binary codes with Hamming distance. Their design is inspired by algorithms introduced in the 90's to construct channel-optimized vector quantizers. At search time, this dual interpretation accelerates the search. Most of the indexed vectors are filtered out with Hamming distance, letting only a fraction of the vectors to be ranked with an asymmetric distance estimator.  The method is complementary with a coarse partitioning of the feature space such as the inverted multi-index. This is shown by our experiments performed on several public benchmarks such as the BIGANN dataset comprising one billion vectors, for which we report state-of-the-art results for query times below 0.3\,millisecond per core. Last but not least, our approach allows the approximate computation of the k-NN graph associated with the Yahoo Flickr Creative Commons 100M, described by CNN image descriptors, in less than 8 hours on a single machine.

Connected through hyperedge HE-1 (induced by source paper "learning to compress and search visual data in large scale systems"):
  with members:
    - "rendition reclaiming what a black box takes away"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "billion scale similarity search with gpus"
    - "hashnet deep learning to hash by continuation"
    - "memnet a persistent memory network for image restoration"
    - "full resolution image compression with recurrent neural networks"
    - "end to end optimized image compression"
    - "a survey on learning to hash"

Connected through hyperedge HE-2 (induced by source paper "unsupervised rank preserving hashing for large scale image retrieval"):
  with members:
    - "link and code fast indexing with graphs and compact regression codes"
    - "deep binary reconstruction for cross modal hashing"
    - "patternnet visual pattern mining with deep neural network"
    - "a survey on learning to hash"
    - "a revisit of hashing algorithms for approximate nearest neighbor search"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "how should we evaluate supervised hashing"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-3 (induced by source paper "scalable place recognition under appearance change for autonomous driving"):
  with members:
    - "semi parametric topological memory for navigation"
    - "dels 3d deep localization and segmentation with a 3d semantic map"
    - "adversarial training for adverse conditions robust metric localisation using appearance transfer"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "geometric loss functions for camera pose regression with deep learning"
    - "benchmarking 6dof urban visual localization in changing conditions"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "geometry aware learning of maps for camera localization"

Connected through hyperedge HE-4 (induced by source paper "quicker adc unlocking the hidden potential of product quantization with simd"):
  with members:
    - "derived codebooks for high accuracy nearest neighbor search"
    - "link and code fast indexing with graphs and compact regression codes"
    - "faster population counts using avx2 instructions"
    - "revisiting the inverted indices for billion scale approximate nearest neighbors"
    - "subic a supervised structured binary code for image search"
    - "billion scale similarity search with gpus"
    - "cuckoo hash tables high performance hash tables for networking applications"
    - "in defense of product quantization"

Connected through hyperedge HE-5 (induced by source paper "derived codebooks for high accuracy nearest neighbor search"):
  with members:
    - "quicker adc unlocking the hidden potential of product quantization with simd"
    - "accelerated nearest neighbor search with quick adc"
    - "searching in one billion vectors re rank with source coding"

Connected through hyperedge HE-6 (induced by source paper "mining on manifolds metric learning without labels"):
  with members:
    - "nearest neighbour radial basis function solvers for deep neural networks"
    - "parametric manifold learning via sparse multidimensional scaling"
    - "sampling matters in deep embedding learning"
    - "transitive invariance for self supervised visual representation learning"
    - "self supervised learning of visual features through embedding images into text topic spaces"
    - "iterative manifold embedding layer learned by incomplete data for large scale image retrieval"
    - "multi task self supervised visual learning"
    - "no fuss distance metric learning using proxies"

Connected through hyperedge HE-7 (induced by source paper "reconfigurable inverted index"):
  with members:
    - "link and code fast indexing with graphs and compact regression 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"
    - "efficient large scale approximate nearest neighbor search on the gpu"

Connected through hyperedge HE-8 (induced by source paper "link and code fast indexing with graphs and compact regression codes"):
  with members:
    - "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"
    - "improving bilayer product quantization for billion scale approximate nearest neighbors in high dimensions"

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)