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: "recent advances in features extraction and description algorithms a comprehensive survey"
  Abstract: Computer vision is one of the most active research fields in information technology today. Giving machines and robots the ability to see and comprehend the surrounding world at the speed of sight creates endless potential applications and opportunities. Feature detection and description algorithms can be indeed considered as the retina of the eyes of such machines and robots. However, these algorithms are typically computationally intensive, which prevents them from achieving the speed of sight real-time performance. In addition, they differ in their capabilities and some may favor and work better given a specific type of input compared to others. As such, it is essential to compactly report their pros and cons as well as their performances and recent advances. This paper is dedicated to provide a comprehensive overview on the state-of-the-art and recent advances in feature detection and description algorithms. Specifically, it starts by overviewing fundamental concepts. It then compares, reports and discusses their performance and capabilities. The Maximally Stable Extremal Regions algorithm and the Scale Invariant Feature Transform algorithms, being two of the best of their type, are selected to report their recent algorithmic derivatives.

Connected through hyperedge HE-1 (induced by source paper "r2d2 repeatable and reliable detector and descriptor"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "key net keypoint detection by handcrafted and learned cnn filters"
    - "lf net learning local features from images"
    - "from handcrafted to deep local invariant features"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "hierarchical metric learning and matching for 2d and 3d geometric correspondences"
    - "local descriptors optimized for average precision"
    - "revisiting oxford and paris large scale image retrieval benchmarking"

Connected through hyperedge HE-2 (induced by source paper "a comparison of cnn and classic features for image retrieval"):
  with members:
    - "lift learned invariant feature transform"
    - "learning covariant feature detectors"
    - "sift meets cnn a decade survey of instance retrieval"
    - "tilde a temporally invariant learned detector"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "learning to assign orientations to feature points"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "an edge computing based architecture for mobile augmented reality"):
  with members:
    - "a survey on mobile edge computing the communication perspective"
    - "energy efficient resource allocation for mobile edge computing based augmented reality applications"
    - "energy efficient resource allocation for mobile edge computation offloading"
    - "collaborative mobile edge computing in 5g networks new paradigms scenarios and challenges"
    - "joint optimization of radio and computational resources for multicell mobile edge computing"

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)