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: "improved person detection on omnidirectional images with non maxima suppression"
  Abstract: We propose a person detector on omnidirectional images, an accurate method to generate minimal enclosing rectangles of persons. The basic idea is to adapt the qualitative detection performance of a convolutional neural network based method, namely YOLOv2 to fish-eye images. The design of our approach picks up the idea of a state-of-the-art object detector and highly overlapping areas of images with their regions of interests. This overlap reduces the number of false negatives. Based on the raw bounding boxes of the detector we fine-tuned overlapping bounding boxes by three approaches: non-maximum suppression, soft non-maximum suppression and soft non-maximum suppression with Gaussian smoothing. The evaluation was done on the PIROPO database and an own annotated Flat dataset, supplemented with bounding boxes on omnidirectional images. We achieve an average precision of 64.4 % with YOLOv2 for the class person on PIROPO and 77.6 % on Flat. For this purpose we fine-tuned the soft non-maximum suppression with Gaussian smoothing.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised multi person action recognition in 360 circ videos"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "unsupervised learning of view invariant action representations"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning spatio temporal features with 3d residual networks for action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "dual glance model for deciphering social relationships"
    - "the kinetics human action video dataset"
    - "r c3d region convolutional 3d network for temporal activity detection"

Connected through hyperedge HE-2 (induced by source paper "gliding vertex on the horizontal bounding box for multi oriented object detection"):
  with members:
    - "arbitrary shape scene text detection with adaptive text region representation"
    - "look more than once an accurate detector for text of arbitrary shapes"
    - "textfield learning a deep direction field for irregular scene text detection"
    - "centernet keypoint triplets for object detection"
    - "cad net a context aware detection network for objects in remote sensing imagery"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "rotation sensitive regression for oriented scene text detection"

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)