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: "fast k nearest neighbor search using gpu"
  Abstract: The recent improvements of graphics processing units (GPU) offer to the computer vision community a powerful processing platform. Indeed, a lot of highly-parallelizable computer vision problems can be significantly accelerated using GPU architecture. Among these algorithms, the k nearest neighbor search (KNN) is a well-known problem linked with many applications such as classification, estimation of statistical properties, etc. The main drawback of this task lies in its computation burden, as it grows polynomially with the data size. In this paper, we show that the use of the NVIDIA CUDA API accelerates the search for the KNN up to a factor of 120.

Connected through hyperedge HE-1 (induced by source paper "deep geometric 6 dof localization from a single image in topo metric maps"):
  with members:
    - "geometric loss functions for camera pose regression with deep learning"
    - "carla an open urban driving simulator"
    - "undeepvo monocular visual odometry through unsupervised deep learning"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "orb slam a versatile and accurate monocular slam system"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "learning densities in feature space for reliable segmentation of indoor scenes"):
  with members:
    - "the fishyscapes benchmark measuring blind spots in semantic segmentation"
    - "volumetric instance aware semantic mapping and 3d object discovery"
    - "panopticfusion online volumetric semantic mapping at the level of stuff and things"
    - "fusion volumetric object level slam"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "mid fusion octree based object level multi instance dynamic slam"

Connected through hyperedge HE-3 (induced by source paper "place deduplication with embeddings"):
  with members:
    - "automatic synonym discovery with knowledge bases"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "attention is all you need"
    - "billion scale similarity search with gpus"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "advances in pre training distributed word representations"
    - "watset automatic induction of synsets from a graph of synonyms"
    - "a deep relevance matching model for ad hoc retrieval"

Connected through hyperedge HE-4 (induced by source paper "fast search with poor ocr"):
  with members:
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "word translation without parallel data"
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"
    - "correlational neural networks"
    - "approximating edit distance in near linear time"

Connected through hyperedge HE-5 (induced by source paper "neural vector spaces for unsupervised information retrieval"):
  with members:
    - "neural networks for information retrieval"
    - "semantic entity retrieval toolkit"
    - "efficient vector representation for documents through corruption"
    - "pyndri a python interface to the indri search engine"
    - "reply with proactive recommendation of email attachments"
    - "a deep relevance matching model for ad hoc retrieval"
    - "learning to match using local and distributed representations of text for web search"
    - "noisy activation functions"

Connected through hyperedge HE-6 (induced by source paper "application driven near data processing for similarity search"):
  with members:
    - "practical and optimal lsh for angular distance"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "caffe convolutional architecture for fast feature embedding"
    - "hitting the memory wall implications of the obvious"

Connected through hyperedge HE-7 (induced by source paper "improving gpu accelerated adaptive idw interpolation algorithm using fast knn search"):
  with members:
    - "accelerating adaptive inverse distance weighting interpolation algorithm on a graphics processing unit"
    - "impact of data layouts on the efficiency of gpu accelerated idw interpolation"
    - "fast k nng construction with gpu based quick multi select"
    - "surface reconstruction from scattered point via rbf interpolation on gpu"

Connected through hyperedge HE-8 (induced by source paper "similarity search on automata processors"):
  with members:
    - "hitting the memory wall implications of the obvious"

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)