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 matching using sift surf brief and orb performance comparison for distorted images"
  Abstract: Fast and robust image matching is a very important task with various applications in computer vision and robotics. In this paper, we compare the performance of three different image matching techniques, i.e., SIFT, SURF, and ORB, against different kinds of transformations and deformations such as scaling, rotation, noise, fish eye distortion, and shearing. For this purpose, we manually apply different types of transformations on original images and compute the matching evaluation parameters such as the number of key points in images, the matching rate, and the execution time required for each algorithm and we will show that which algorithm is the best more robust against each kind of distortion. Index Terms-Image matching, scale invariant feature transform (SIFT), speed up robust feature (SURF), robust independent elementary features (BRIEF), oriented FAST, rotated BRIEF (ORB).

Connected through hyperedge HE-1 (induced by source paper "estimating a null model of scientific image reuse to support research integrity investigations"):
  with members:
    - "lf net learning local features from images"
    - "faster and better a machine learning approach to corner detection"

Connected through hyperedge HE-2 (induced by source paper "learning pose estimation for uav autonomous navigation and landing using visual inertial sensor data"):
  with members:
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "geometric loss functions for camera pose regression with deep learning"
    - "se3 pose nets structured deep dynamics models for visuomotor planning and control"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "visual servoing from deep neural networks"
    - "visual inertial monocular slam with map reuse"

Connected through hyperedge HE-3 (induced by source paper "monocular camera based fruit counting and mapping with semantic data association"):
  with members:
    - "semantic mapping for orchard environments by merging two sides reconstructions of tree rows"
    - "robust fruit counting combining deep learning tracking and structure from motion"
    - "vision based preharvest yield mapping for apple orchards"
    - "fruit quantity and quality estimation using a robotic vision system"
    - "image segmentation for fruit detection and yield estimation in apple orchards"
    - "deep fruit detection in orchards"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "retrieving similar e commerce images using deep learning"):
  with members:
    - "deep metric learning with angular loss"
    - "analyzing the performance of multilayer neural networks for object recognition"
    - "how transferable are features in deep neural networks"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "sub pixel matching method for low resolution thermal stereo images"):
  with members:
    - "faster and better a machine learning approach to corner detection"

Connected through hyperedge HE-6 (induced by source paper "image similarity using deep cnn and curriculum learning"):
  with members:
    - "recent advance in content based image retrieval a literature survey"
    - "smart mining for deep metric learning"
    - "unsupervised learning of visual representations using videos"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "learning to compare image patches via convolutional neural networks"
    - "a practical guide to cnns and fisher vectors for image instance retrieval"
    - "very deep convolutional networks for large scale image recognition"
    - "learning fine grained image similarity with deep ranking"

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)