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 spectral ranking for similarity search"
  Abstract: Despite the success of deep learning on representing images for particular object retrieval, recent studies show that the learned representations still lie on manifolds in a high dimensional space. This makes the Euclidean nearest neighbor search biased for this task. Exploring the manifolds online remains expensive even if a nearest neighbor graph has been computed offline. This work introduces an explicit embedding reducing manifold search to Euclidean search followed by dot product similarity search. This is equivalent to linear graph filtering of a sparse signal in the frequency domain. To speed up online search, we compute an approximate Fourier basis of the graph offline. We improve the state of art on particular object retrieval datasets including the challenging Instre dataset containing small objects. At a scale of 10^5 images, the offline cost is only a few hours, while query time is comparable to standard similarity search.

Connected through hyperedge HE-1 (induced by source paper "smooth adversarial examples"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "adversarial attacks and defences competition"
    - "harmonic adversarial attack method"
    - "on the suitability of l_p norms for creating and preventing adversarial examples"
    - "a closed form solution to photorealistic image stylization"
    - "a flexible convolutional solver with application to photorealistic style transfer"
    - "low frequency adversarial perturbation"
    - "learning random walk label propagation for weakly supervised semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "mining on manifolds metric learning without labels"):
  with members:
    - "nearest neighbour radial basis function solvers for deep neural networks"
    - "polysemous codes"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "deep feature aggregation and image re ranking with heat diffusion for image retrieval"):
  with members:
    - "unsupervised part based weighting aggregation of deep convolutional features for image retrieval"
    - "selective deep convolutional features for image retrieval"
    - "siamese network of deep fisher vector descriptors for image retrieval"
    - "voronoi based compact image descriptors efficient region of interest retrieval with vlad and deep learning based descriptors"
    - "deep image retrieval learning global representations for image search"
    - "embedding based on function approximation for large scale image search"
    - "large scale image retrieval with attentive deep local features"
    - "efficient diffusion on region manifolds recovering small objects with compact cnn representations"

Connected through hyperedge HE-4 (induced by source paper "detect to retrieve efficient regional aggregation for image search"):
  with members:
    - "local features and visual words emerge in activations"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "repeatability is not enough learning affine regions via discriminability"
    - "fine tuning cnn image retrieval with no human annotation"
    - "working hard to know your neighbor s margins local descriptor learning loss"
    - "deep image retrieval learning global representations for image search"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-5 (induced by source paper "revisiting oxford and paris large scale image retrieval benchmarking"):
  with members:
    - "fine tuning cnn image retrieval with no human annotation"
    - "end to end learning of deep visual representations for image retrieval"
    - "bags of local convolutional features for scalable instance search"
    - "large scale image retrieval with attentive deep local features"
    - "efficient diffusion on region manifolds recovering small objects with compact cnn representations"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"
    - "yfcc100m the new data in multimedia research"
    - "tilde a temporally invariant learned detector"

Connected through hyperedge HE-6 (induced by source paper "efficient image retrieval via decoupling diffusion into online and offline processing"):
  with members:
    - "mining on manifolds metric learning without labels"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "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"
    - "efficient diffusion on region manifolds recovering small objects with compact cnn representations"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"
    - "particular object retrieval with integral max pooling of cnn activations"

Connected through hyperedge HE-7 (induced by source paper "hybrid diffusion spectral temporal graph filtering for manifold ranking"):
  with members:
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "fine tuning cnn image retrieval with no human annotation"
    - "end to end learning of deep visual representations for image retrieval"
    - "efficient diffusion on region manifolds recovering small objects with compact cnn representations"
    - "cross dimensional weighting for aggregated deep convolutional features"
    - "particular object retrieval with integral max pooling of cnn activations"

Connected through hyperedge HE-8 (induced by source paper "an evaluation of large scale methods for image instance and class discovery"):
  with members:
    - "billion scale similarity search with gpus"
    - "deep image retrieval learning global representations for image search"
    - "loh and behold web scale visual search recommendation and clustering using locally optimized hashing"
    - "planet photo geolocation with convolutional neural networks"
    - "efficient diffusion on region manifolds recovering small objects with compact cnn representations"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"
    - "yfcc100m the new data in multimedia research"
    - "deep residual learning for image recognition"

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)