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: "memory vectors for similarity search in high dimensional spaces"
  Abstract: We study an indexing architecture to store and search in a database of high-dimensional vectors from the perspective of statistical signal processing and decision theory. This architecture is composed of several memory units, each of which summarizes a fraction of the database by a single representative vector. The potential similarity of the query to one of the vectors stored in the memory unit is gauged by a simple correlation with the memory unit's representative vector. This representative optimizes the test of the following hypothesis: the query is independent from any vector in the memory unit vs. the query is a simple perturbation of one of the stored vectors. #R##N#Compared to exhaustive search, our approach finds the most similar database vectors significantly faster without a noticeable reduction in search quality. Interestingly, the reduction of complexity is provably better in high-dimensional spaces. We empirically demonstrate its practical interest in a large-scale image search scenario with off-the-shelf state-of-the-art descriptors.

Connected through hyperedge HE-1 (induced by source paper "exquisitor interactive learning at large"):
  with members:
    - "revisiting the inverted indices for billion scale approximate nearest neighbors"
    - "cost effective active learning for deep image classification"
    - "approximate furthest neighbor with application to annulus query"
    - "active decision boundary annotation with deep generative models"
    - "a survey on learning to hash"
    - "yfcc100m the new data in multimedia research"
    - "whittlesearch interactive image search with relative attribute feedback"
    - "going deeper with convolutions"

Connected through hyperedge HE-2 (induced by source paper "aggregated deep local features for remote sensing image retrieval"):
  with members:
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "learning low dimensional convolutional neural networks for high resolution remote sensing image retrieval"
    - "remote sensing image scene classification benchmark and state of the art"
    - "large scale image retrieval with attentive deep local features"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "deep residual learning for image recognition"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "neural codes for image retrieval"

Connected through hyperedge HE-3 (induced by source paper "privacy preserving group membership verification and identification"):
  with members:
    - "ghostvlad for set based face recognition"
    - "approximate two party privacy preserving string matching with linear complexity"

Connected through hyperedge HE-4 (induced by source paper "deja vu an empirical evaluation of the memorization properties of convnets"):
  with members:
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "understanding membership inferences on well generalized learning models"
    - "the case for learned index structures"
    - "deep image prior"
    - "billion scale similarity search with gpus"
    - "cut paste and learn surprisingly easy synthesis for instance detection"
    - "logan membership inference attacks against generative models"
    - "ada a game theoretic perspective on data augmentation for object detection"

Connected through hyperedge HE-5 (induced by source paper "local orthogonal group testing"):
  with members:
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "subic a supervised structured binary code for image search"
    - "billion scale similarity search with gpus"
    - "fine tuning cnn image retrieval with no human annotation"
    - "learning a complete image indexing pipeline"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "fast spectral ranking for similarity search"):
  with members:
    - "unifying local and non local signal processing with graph cnns"
    - "geometric deep learning going beyond euclidean data"
    - "end to end learning of deep visual representations for image retrieval"
    - "deep image retrieval learning global representations for image search"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "efficient diffusion on region manifolds recovering small objects with compact cnn representations"
    - "fast exact and multi scale inference for semantic image segmentation with deep gaussian crfs"

Connected through hyperedge HE-7 (induced by source paper "deep region hashing for efficient large scale instance search from images"):
  with members:
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "faster r cnn features for instance search"
    - "very deep convolutional networks for natural language processing"
    - "a survey on learning to hash"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "exploiting local features from deep networks for image retrieval"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-8 (induced by source paper "neural distributed autoassociative memories a survey"):
  with members:
    - "autoscaling bloom filter controlling trade off between true and false positives"
    - "dense associative memory is robust to adversarial inputs"
    - "fast similarity sketching"
    - "optimal las vegas locality sensitive data structures"
    - "associative memories to accelerate approximate nearest neighbor search"
    - "dense associative memory for pattern recognition"
    - "set similarity search beyond minhash"
    - "distance sensitive bloom filters without false negatives"

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)