human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "utilizing temporal information in deep convolutional network for efficient soccer ball detection and tracking"):
  Abstract: Soccer ball detection is identified as one of the critical challenges in the RoboCup competition. It requires an efficient vision system capable of handling the task of detection with high precision and recall and providing robust and low inference time. In this work, we present a novel convolutional neural network (CNN) approach to detect the soccer ball in an image sequence. In contrast to the existing methods where only the current frame or an image is used for the detection, we make use of the history of frames. Using history allows to efficiently track the ball in situations where the ball disappears or gets partially occluded in some of the frames. Our approach exploits spatio-temporal correlation and detects the ball based on the trajectory of its movements. We present our results with three convolutional methods, namely temporal convolutional networks (TCN), ConvLSTM, and ConvGRU. We first solve the detection task for an image using fully convolutional encoder-decoder architecture, and later, we use it as an input to our temporal models and jointly learn the detection task in sequences of images. We evaluate all our experiments on a novel dataset prepared as a part of this work. Furthermore, we present empirical results to support the effectiveness of using the history of the ball in challenging scenarios.

Includes members:
  Member "frequency domain transformer networks for video prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pisep 2 pseudo image sequence evolution based 3d pose prediction")
  Member "playing soccer without colors in the spl a convolutional neural network approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "xyolo a model for real time object detection in humanoid soccer on low end hardware")
  Member "deep learning for semantic segmentation on minimal hardware":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "closing the reality gap with unsupervised sim to real image translation for semantic segmentation in robot soccer")
  Member "a monocular vision system for playing soccer in low color information environments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "nimbro robots winning robocup 2018 humanoid adultsize soccer competitions")
  Member "nimbro op2x adult sized open source 3d printed humanoid robot":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "nimbro robots winning robocup 2018 humanoid adultsize soccer competitions")
  Member "visual mesh real time object detection using constant sample density":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robo robust fully neural object detection for robot soccer")
  Member "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "blank language models")
  Member "quo vadis action recognition a new model and the kinetics dataset":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "transformation based adversarial video prediction on large scale data")

Question: Please tell me which class the source paper 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)