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: "playing soccer without colors in the spl a convolutional neural network approach"
  Abstract: The goal of this paper is to propose a vision system for humanoid robotic soccer that does not use any color information. The main features of this system are: (i) real-time operation in the NAO robot, and (ii) the ability to detect the ball, the robots, their orientations, the lines and key field features robustly. Our ball detector, robot detector, and robot's orientation detector obtain the highest reported detection rates. The proposed vision system is tested in a SPL field with several NAO robots under realistic and highly demanding conditions. The obtained results are: robot detection rate of 94.90%, ball detection rate of 97.10%, and a completely perceived orientation rate of 99.88% when the observed robot is static, and 95.52% when the observed robot is moving.

Connected through hyperedge HE-1 (induced by source paper "utilizing temporal information in deep convolutional network for efficient soccer ball detection and tracking"):
  with members:
    - "frequency domain transformer networks for video prediction"
    - "deep learning for semantic segmentation on minimal hardware"
    - "a monocular vision system for playing soccer in low color information environments"
    - "nimbro op2x adult sized open source 3d printed humanoid robot"
    - "visual mesh real time object detection using constant sample density"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "temporal convolutional networks for action segmentation and detection"

Connected through hyperedge HE-2 (induced by source paper "xyolo a model for real time object detection in humanoid soccer on low end hardware"):
  with members:
    - "tiny dsod lightweight object detection for resource restricted usages"
    - "deep learning for semantic segmentation on minimal hardware"
    - "using convolutional neural networks in robots with limited computational resources detecting nao robots while playing soccer"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "ssd single shot multibox detector"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "you only look once unified real time object detection"
    - "going deeper with convolutions"

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)