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: "mavot memory augmented video object tracking"
  Abstract: We introduce a one-shot learning approach for video object tracking. The proposed algorithm requires seeing the object to be tracked only once, and employs an external memory to store and remember the evolving features of the foreground object as well as backgrounds over time during tracking. With the relevant memory retrieved and updated in each tracking, our tracking model is capable of maintaining long-term memory of the object, and thus can naturally deal with hard tracking scenarios including partial and total occlusion, motion changes and large scale and shape variations. In our experiments we use the ImageNet ILSVRC2015 video detection dataset to train and use the VOT-2016 benchmark to test and compare our Memory-Augmented Video Object Tracking (MAVOT) model. From the results, we conclude that given its oneshot property and simplicity in design, MAVOT is an attractive approach in visual tracking because it shows good performance on VOT-2016 benchmark and is among the top 5 performers in accuracy and robustness in occlusion, motion changes and empty target.

Connected through hyperedge HE-1 (induced by source paper "visual tracking via dynamic memory networks"):
  with members:
    - "a twofold siamese network for real time object tracking"
    - "learning dynamic memory networks for object tracking"
    - "recurrent filter learning for visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "learning spatial aware regressions for visual tracking"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "dual deep network for visual tracking"
    - "large margin object tracking with circulant feature maps"

Connected through hyperedge HE-2 (induced by source paper "dawn dual augmented memory network for unsupervised video object tracking"):
  with members:
    - "distractor aware siamese networks for visual object tracking"
    - "learning dynamic memory networks for object tracking"
    - "memory matching networks for one shot image recognition"
    - "real time visual tracking by deep reinforced decision making"
    - "deep reinforcement learning for visual object tracking in videos"
    - "online multi object tracking using cnn based single object tracker with spatial temporal attention mechanism"
    - "multi context attention for human pose estimation"
    - "residual attention network for image classification"

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)