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: "image similarity using deep cnn and curriculum learning"
  Abstract: Image similarity involves fetching similar looking images given a reference image. Our solution called SimNet, is a deep siamese network which is trained on pairs of positive and negative images using a novel online pair mining strategy inspired by Curriculum learning. We also created a multi-scale CNN, where the final image embedding is a joint representation of top as well as lower layer embedding's. We go on to show that this multi-scale siamese network is better at capturing fine grained image similarities than traditional CNN's.

Connected through hyperedge HE-1 (induced by source paper "oreos oriented recognition of 3d point clouds in outdoor scenarios"):
  with members:
    - "segmap 3d segment mapping using data driven descriptors"
    - "a comprehensive review of 3d point cloud descriptors"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "segmatch segment based place recognition in 3d point clouds"
    - "locnet global localization in 3d point clouds for mobile vehicles"
    - "cnn for imu assisted odometry estimation using velodyne lidar"
    - "vehicle detection from 3d lidar using fully convolutional network"

Connected through hyperedge HE-2 (induced by source paper "getnet get target area for image pairing"):
  with members:
    - "deep spectral correspondence for matching disparate image pairs"
    - "scone siamese constellation embedding descriptor for image matching"
    - "deep image retrieval learning global representations for image search"
    - "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"
    - "spatial transformer networks"
    - "visual instance retrieval with deep convolutional networks"
    - "neural codes for image retrieval"

Connected through hyperedge HE-3 (induced by source paper "scalable logo recognition using proxies"):
  with members:
    - "yolov3 an incremental improvement"
    - "arcface additive angular margin loss for deep face recognition"
    - "open logo detection challenge"
    - "focal loss for dense object detection"
    - "deep learning for logo recognition"
    - "squeeze and excitation networks"
    - "no fuss distance metric learning using proxies"
    - "open set logo detection and 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)