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: "interferences in match kernels"
  Abstract: We consider the design of an image representation that embeds and aggregates a set of local descriptors into a single vector. Popular representations of this kind include the bag-of-visual-words, the Fisher vector and the VLAD. When two such image representations are compared with the dot-product, the image-to-image similarity can be interpreted as a match kernel. In match kernels, one has to deal with interference, i.e. with the fact that even if two descriptors are unrelated, their matching score may contribute to the overall similarity. #R##N#We formalise this problem and propose two related solutions, both aimed at equalising the individual contributions of the local descriptors in the final representation. These methods modify the aggregation stage by including a set of per-descriptor weights. They differ by the objective function that is optimised to compute those weights. The first is a "democratisation" strategy that aims at equalising the relative importance of each descriptor in the set comparison metric. The second one involves equalising the match of a single descriptor to the aggregated vector. #R##N#These concurrent methods give a substantial performance boost over the state of the art in image search with short or mid-size vectors, as demonstrated by our experiments on standard public image retrieval benchmarks.

Connected through hyperedge HE-1 (induced by source paper "deep generalized max pooling"):
  with members:
    - "deep adaptive learning for writer identification based on single handwritten word images"
    - "semi supervised feature learning for improving writer identification"
    - "on the convergence of adam and beyond"
    - "precision learning towards use of known operators in neural networks"
    - "in defense of the triplet loss for person re identification"
    - "unsupervised feature learning for writer identification and writer retrieval"
    - "encoding cnn activations for writer recognition"
    - "convolutional neural networks for font classification"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "second order democratic aggregation"):
  with members:
    - "a deeper look at power normalizations"
    - "statistically motivated second order pooling"
    - "is second order information helpful for large scale visual recognition"
    - "improved bilinear pooling with cnns"
    - "second order convolutional neural networks"
    - "tensor representations via kernel linearization for action recognition from 3d skeletons"
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"

Connected through hyperedge HE-4 (induced by source paper "generalized orderless pooling performs implicit salient matching"):
  with members:
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "weakly supervised fine grained categorization with part based image representation"
    - "visual concept recognition and localization via iterative introspection"
    - "understanding neural networks through deep visualization"
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "encoding cnn activations for writer recognition"):
  with members:
    - "selective deep convolutional features for image retrieval"
    - "identity mappings in deep residual networks"
    - "deepwriter a multi stream deep cnn for text independent writer identification"
    - "convolutional patch representations for image retrieval an unsupervised approach"
    - "deep residual learning for image recognition"
    - "exploiting local features from deep networks for image retrieval"
    - "generalized max pooling"
    - "multi scale orderless pooling of deep convolutional activation 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)