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: "siamese network of deep fisher vector descriptors for image retrieval"
  Abstract: This paper addresses the problem of large scale image retrieval, with the aim of accurately ranking the similarity of a large number of images to a given query image. To achieve this, we propose a novel Siamese network. This network consists of two computational strands, each comprising of a CNN component followed by a Fisher vector component. The CNN component produces dense, deep convolutional descriptors that are then aggregated by the Fisher Vector method. Crucially, we propose to simultaneously learn both the CNN filter weights and Fisher Vector model parameters. This allows us to account for the evolving distribution of deep descriptors over the course of the learning process. We show that the proposed approach gives significant improvements over the state-of-the-art methods on the Oxford and Paris image retrieval datasets. Additionally, we provide a baseline performance measure for both these datasets with the inclusion of 1 million distractors.

Connected through hyperedge HE-1 (induced by source paper "targeted mismatch adversarial attack query with a flower to retrieve the tower"):
  with members:
    - "who s afraid of adversarial queries the impact of image modifications on content based image retrieval"
    - "universal perturbation attack against image retrieval"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "open set adversarial examples"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "bags of local convolutional features for scalable instance search"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"

Connected through hyperedge HE-2 (induced by source paper "travelgan image to image translation by transformation vector learning"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "magan aligning biological manifolds"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "cycada cycle consistent adversarial domain adaptation"
    - "combogan unrestrained scalability for image domain translation"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "from source to target and back symmetric bi directional adaptive gan"

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"
    - "fast spectral ranking for similarity search"
    - "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 "fine tuning cnn image retrieval with no human annotation"):
  with members:
    - "end to end learning of deep visual representations for image retrieval"
    - "bags of local convolutional features for scalable instance search"
    - "deep image retrieval learning global representations for image search"
    - "good practice in cnn feature transfer"
    - "nested invariance pooling and rbm hashing for image instance retrieval"
    - "large scale image retrieval with attentive deep local features"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"
    - "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)