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 adaptive descriptor design for object recognition in the wild"
  Abstract: Digital images nowadays show large appearance variabilities on picture styles, in terms of color tone, contrast, vignetting, and etc. These `picture styles' are directly related to the scene radiance, image pipeline of the camera, and post processing functions (e.g., photography effect filters). Due to the complexity and nonlinearity of these factors, popular gradient-based image descriptors generally are not invariant to different picture styles, which could degrade the performance for object recognition. Given that images shared online or created by individual users are taken with a wide range of devices and may be processed by various post processing functions, to find a robust object recognition system is useful and challenging. In this paper, we investigate the influence of picture styles on object recognition by making a connection between image descriptors and a pixel mapping function g, and accordingly propose an adaptive approach based on a g-incorporated kernel descriptor and multiple kernel learning, without estimating or specifying the image styles used in training and testing. We conduct experiments on the Domain Adaptation data set, the Oxford Flower data set, and several variants of the Flower data set by introducing popular photography effects through post-processing. The results demonstrate that the proposed method consistently yields recognition improvements over standard descriptors in all studied cases.

Connected through hyperedge HE-1 (induced by source paper "photo filter recommendation by category aware aesthetic learning"):
  with members:
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "learning fine grained image similarity with deep ranking"
    - "deep metric learning using triplet network"

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)