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: "modular tracking framework a unified approach to registration based tracking"
  Abstract: This paper presents a modular, extensible and highly efficient open source framework for registration based tracking called Modular Tracking Framework (MTF). Targeted at robotics applications, it is implemented entirely in C++ and designed from the ground up to easily integrate with systems that support any of several major vision and robotics libraries including OpenCV, ROS, ViSP and Eigen. It implements more methods, is faster, and more precise than other existing systems. Further, the theoretical basis for its design is a new way to conceptualize registration based trackers that decomposes them into three constituent sub modules - Search Method (SM), Appearance Model (AM) and State Space Model (SSM). #R##N#In the process, we integrate many important advances published after Baker \& Matthews' landmark work in 2004. In addition to being a practical solution for fast and high precision tracking, MTF can also serve as a useful research tool by allowing existing and new methods for any of the sub modules to be studied better. When a new method is introduced for one of these, the breakdown can help to experimentally find the combination of methods for the others that is optimum for it. By extensive use of generic programming, MTF makes it easy to plug in a new method for any of the sub modules so that it can not only be tested comprehensively with existing methods but also become immediately available for deployment in any project that uses the framework. With 16 AMs, 11 SMs and 13 SSMs implemented already, MTF provides over 2000 distinct single layer trackers. It also allows two or more of these to be combined together in several ways to create a practically unlimited variety of novel multi layer trackers.

Connected through hyperedge HE-1 (induced by source paper "planar object tracking in the wild a benchmark"):
  with members:
    - "learning multi domain convolutional neural networks for visual tracking"
    - "metric learning driven multi task structured output optimization for robust keypoint tracking"
    - "faster and better a machine learning approach to corner detection"

Connected through hyperedge HE-2 (induced by source paper "4 dof tracking for robot fine manipulation tasks"):
  with members:
    - "learning to track at 100 fps with deep regression networks"

Connected through hyperedge HE-3 (induced by source paper "unifying registration based tracking a case study with structural similarity"):
  with members:
    - "modular decomposition and analysis of registration based trackers"
    - "learning to track at 100 fps with deep regression networks"
    - "high speed tracking with kernelized correlation filters"

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)