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: "efficient large scale approximate nearest neighbor search on the gpu"
  Abstract: We present a new approach for efficient approximate nearest neighbor (ANN) search in high dimensional spaces, extending the idea of Product Quantization. We propose a two level product and vector quantization tree that reduces the number of vector comparisons required during tree traversal. Our approach also includes a novel highly parallelizable re-ranking method for candidate vectors by efficiently reusing already computed intermediate values. Due to its small memory footprint during traversal the method lends itself to an efficient, parallel GPU implementation. This Product Quantization Tree (PQT) approach significantly outperforms recent state of the art methods for high dimensional nearest neighbor queries on standard reference datasets. Ours is the first work that demonstrates GPU performance superior to CPU performance on high dimensional, large scale ANN problems in time-critical real-world applications, like loop-closing in videos.

Connected through hyperedge HE-1 (induced by source paper "kidnapped radar topological radar localisation using rotationally invariant metric learning"):
  with members:
    - "masking by moving learning distraction free radar odometry from pose information"
    - "the oxford radar robotcar dataset a radar extension to the oxford robotcar dataset"
    - "making convolutional networks shift invariant again"
    - "radar only ego motion estimation in difficult settings via graph matching"
    - "a holistic visual place recognition approach using lightweight cnns for severe viewpoint and appearance changes"
    - "omnidirectional cnn for visual place recognition and navigation"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "local descriptor for robust place recognition using lidar intensity"

Connected through hyperedge HE-2 (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"
    - "deep residual learning for image recognition"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "improving bilayer product quantization for billion scale approximate nearest neighbors in high dimensions"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "searching in one billion vectors re rank with source coding"

Connected through hyperedge HE-3 (induced by source paper "ggnn graph based gpu nearest neighbor search"):
  with members:
    - "vector and line quantization for billion scale similarity search on gpus"
    - "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"
    - "searching in one billion vectors re rank with source coding"

Connected through hyperedge HE-4 (induced by source paper "accurate and efficient similarity search for large scale face recognition"):
  with members:
    - "normface l 2 hypersphere embedding for face verification"
    - "contrastive center loss for deep neural networks"
    - "billion scale similarity search with gpus"
    - "aggregated residual transformations for deep neural networks"
    - "deep image retrieval learning global representations for image search"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (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-6 (induced by source paper "recurrent binary embedding for gpu enabled exhaustive retrieval from billion scale semantic vectors"):
  with members:
    - "deep learning as a mixed convex combinatorial optimization problem"
    - "billion scale similarity search with gpus"
    - "deep embedding forest forest based serving with deep embedding features"
    - "hashnet deep learning to hash by continuation"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "ternary neural networks for resource efficient ai applications"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (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"
    - "approximate search with quantized sparse representations"
    - "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-8 (induced by source paper "speeding up the verification phase of set similarity joins in the gpgpu paradigm"):
  with members:
    - "flash randomized algorithms accelerated over cpu gpu for ultra high dimensional similarity search"
    - "billion scale similarity search with gpus"
    - "a dynamic hash table for the gpu"
    - "a generic inverted index framework for similarity search on the gpu technical report"
    - "v smart join a scalable mapreduce framework for all pair similarity joins of multisets and vectors"

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)