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 image retrieval via decoupling diffusion into online and offline processing"
  Abstract: Diffusion is commonly used as a ranking or re-ranking method in retrieval tasks to achieve higher retrieval performance, and has attracted lots of attention in recent years. A downside to diffusion is that it performs slowly in comparison to the naive k-NN search, which causes a non-trivial online computational cost on large datasets. To overcome this weakness, we propose a novel diffusion technique in this paper. In our work, instead of applying diffusion to the query, we pre-compute the diffusion results of each element in the database, making the online search a simple linear combination on top of the k-NN search process. Our proposed method becomes 10~ times faster in terms of online search speed. Moreover, we propose to use late truncation instead of early truncation in previous works to achieve better retrieval performance.

Connected through hyperedge HE-1 (induced by source paper "a benchmark on tricks for large scale image retrieval"):
  with members:
    - "semi supervised segmentation of salt bodies in seismic images using an ensemble of convolutional neural networks"
    - "semi supervised feature level attribute manipulation for fashion image retrieval"
    - "combination of multiple global descriptors for image retrieval"
    - "multigrain a unified image embedding for classes and instances"
    - "efficient nearest neighbors search for large scale landmark recognition"
    - "bag of tricks for image classification with convolutional neural networks"
    - "batch dropblock network for person re identification and beyond"
    - "harmonious attention network for person re identification"

Connected through hyperedge HE-2 (induced by source paper "large scale landmark retrieval recognition under a noisy and diverse dataset"):
  with members:
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "billion scale similarity search with gpus"
    - "squeeze and excitation networks"
    - "fine tuning cnn image retrieval with no human annotation"
    - "sgdr stochastic gradient descent with restarts"

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"
    - "an accurate retrieval through r mac descriptors for landmark recognition"
    - "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"

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)