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: "an accurate retrieval through r mac descriptors for landmark recognition"
  Abstract: The landmark recognition problem is far from being solved, but with the use of features extracted from intermediate layers of Convolutional Neural Networks (CNNs), excellent results have been obtained. In this work, we propose some improvements on the creation of R-MAC descriptors in order to make the newly-proposed R-MAC+ descriptors more representative than the previous ones. However, the main contribution of this paper is a novel retrieval technique, that exploits the fine representativeness of the MAC descriptors of the database images. Using this descriptors called "db regions" during the retrieval stage, the performance is greatly improved. The proposed method is tested on different public datasets: Oxford5k, Paris6k and Holidays. It outperforms the state-of-the- art results on Holidays and reached excellent results on Oxford5k and Paris6k, overcame only by approaches based on fine-tuning strategies.

Connected through hyperedge HE-1 (induced by source paper "deep aggregation of regional convolutional activations for content based image retrieval"):
  with members:
    - "fine tuning cnn image retrieval with no human annotation"
    - "what are the receptive effective receptive and projective fields of neurons in convolutional neural networks"
    - "end to end learning of deep visual representations for image retrieval"
    - "identity mappings in deep residual networks"
    - "deep metric learning via facility location"
    - "large scale image retrieval with attentive deep local features"
    - "deep residual learning for image recognition"
    - "particular object retrieval with integral max pooling of cnn activations"

Connected through hyperedge HE-2 (induced by source paper "an efficient approximate knn graph method for diffusion on image retrieval"):
  with members:
    - "mining on manifolds metric learning without labels"
    - "low shot learning with large scale diffusion"
    - "iterative manifold embedding layer learned by incomplete data for large scale image retrieval"
    - "end to end learning of deep visual representations for image retrieval"
    - "a survey on learning to hash"
    - "efficient diffusion on region manifolds recovering small objects with compact cnn representations"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "hashing for similarity search a survey"

Connected through hyperedge HE-3 (induced by source paper "genetic algorithms for the optimization of diffusion parameters in content based image retrieval"):
  with members:
    - "an efficient approximate knn graph method for diffusion on image retrieval"
    - "efficient image retrieval via decoupling diffusion into online and offline processing"
    - "investigating the parameter space of evolutionary algorithms"
    - "bohb robust and efficient hyperparameter optimization at scale"
    - "end to end learning of deep visual representations for image retrieval"
    - "deep image retrieval learning global representations for image search"
    - "efficient diffusion on region manifolds recovering small objects with compact cnn representations"
    - "cross dimensional weighting for aggregated deep convolutional features"

Connected through hyperedge HE-4 (induced by source paper "a dense depth representation for vlad descriptors in content based image retrieval"):
  with members:
    - "a location aware embedding technique for accurate landmark recognition"
    - "end to end learning of deep visual representations for image retrieval"
    - "deep image retrieval learning global representations for image search"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "object level deep feature pooling for compact image representation"
    - "deep residual learning for image recognition"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "exploiting local features from deep networks for image retrieval"

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)