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: "machine learning for realisticball detection in robocup spl"
  Abstract: In this technical report, we describe the use of a machine learning approach for detecting the realistic black and white ball currently in use in the RoboCup Standard Platform League. Our aim is to provide a ready-to-use software module that can be useful for the RoboCup SPL community. To this end, the approach is integrated within the official B-Human code release 2016. The complete code for the approach presented in this work can be downloaded from the SPQR Team homepage at this http URL and from the SPQR Team GitHub repository at this https URL. The approach has been tested in multiple environments, both indoor and outdoor. Furthermore, the ball detector described in this technical report has been used by the SPQR Robot Soccer Team during the competitions of the Robocup German Open 2017. To facilitate the use of our code by other teams, we have prepared a step-by-step installation guide.

Connected through hyperedge HE-1 (induced by source paper "visual mesh real time object detection using constant sample density"):
  with members:
    - "yolov3 an incremental improvement"
    - "self normalizing neural networks"
    - "using convolutional neural networks in robots with limited computational resources detecting nao robots while playing soccer"
    - "yolo9000 better faster stronger"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "tensorflow a system for large scale machine learning"
    - "you only look once unified real time object detection"
    - "fast and accurate deep network learning by exponential linear units elus"

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)