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: "x linenet detecting aircraft in remote sensing images by a pair of intersecting line segments"
  Abstract: In the field of aircraft detection, tremendous progress has been gained movitated by the development of deep convolution neural networks(DCNNs). At present, most state-of-art models based on DCNNs belong to top-down approaches which take a wide use of anchor mechanism. The obtaining of high accuracy in them relys on the enumeration of massive potentional locations of objects with the form of rectangular bounding box, which is wasteful and less elaborate. In this paper, we present a novel aircraft detection model in a bottom-up manner, which formulated the task as detection of two intersecting line segments inside each target and grouping of them, thus we name it as X-LineNet. As the result of learning more delicate visual grammars information of aircraft, detection results with more concrete details and higher accuracy can be gained by X-LineNet. Just for these advantages, we designed a novel form of detetction results--pentagonal mask which has less redundancy and can better represent airplanes than that of rectangular box in remote sensing images.

Connected through hyperedge HE-1 (induced by source paper "objects detection for remote sensing images based on polar coordinates"):
  with members:
    - "fcos fully convolutional one stage object detection"
    - "objects as points"
    - "oriented objects as pairs of middle lines"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "scrdet towards more robust detection for small cluttered and rotated objects"
    - "yolov3 an incremental improvement"

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)