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: "recurrent filter learning for visual tracking"
  Abstract: Recently using convolutional neural networks (CNNs) has gained popularity in visual tracking, due to its robust feature representation of images. Recent methods perform online tracking by fine-tuning a pre-trained CNN model to the specific target object using stochastic gradient descent (SGD) back-propagation, which is usually time-consuming. In this paper, we propose a recurrent filter generation methods for visual tracking. We directly feed the target's image patch to a recurrent neural network (RNN) to estimate an object-specific filter for tracking. As the video sequence is a spatiotemporal data, we extend the matrix multiplications of the fully-connected layers of the RNN to a convolution operation on feature maps, which preserves the target's spatial structure and also is memory-efficient. The tracked object in the subsequent frames will be fed into the RNN to adapt the generated filters to appearance variations of the target. Note that once the off-line training process of our network is finished, there is no need to fine-tune the network for specific objects, which makes our approach more efficient than methods that use iterative fine-tuning to online learn the target. Extensive experiments conducted on widely used benchmarks, OTB and VOT, demonstrate encouraging results compared to other recent methods.

Connected through hyperedge HE-1 (induced by source paper "object adaptive lstm network for real time visual tracking with adversarial data augmentation"):
  with members:
    - "quadruplet network with one shot learning for fast visual object tracking"
    - "deep visual attention prediction"
    - "context aware deep feature compression for high speed visual tracking"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "video salient object detection via fully convolutional networks"
    - "vital visual tracking via adversarial learning"
    - "learning dynamic memory networks for object tracking"

Connected through hyperedge HE-2 (induced by source paper "a review of visual trackers and analysis of its application to mobile robot"):
  with members:
    - "long term tracking in the wild a benchmark"
    - "context aware deep feature compression for high speed visual tracking"
    - "adaptive correlation filters with long term and short term memory for object tracking"
    - "joint representation and truncated inference learning for correlation filter based tracking"
    - "a twofold siamese network for real time object tracking"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "good features to correlate for visual tracking"
    - "unveiling the power of deep tracking"

Connected through hyperedge HE-3 (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"
    - "end to end representation learning for correlation filter based tracking"
    - "mavot memory augmented video object 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-4 (induced by source paper "roam recurrently optimizing tracking model"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "deeper and wider siamese networks for real time visual tracking"
    - "real time mdnet"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "siamrpn evolution of siamese visual tracking with very deep networks"
    - "vital visual tracking via adversarial learning"

Connected through hyperedge HE-5 (induced by source paper "learning reinforced attentional representation for end to end visual tracking"):
  with members:
    - "siamese attentional keypoint network for high performance visual tracking"
    - "learning cascaded siamese networks for high performance visual tracking"
    - "context aware deep feature compression for high speed visual tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "efficient multi level correlating for visual tracking"
    - "a twofold siamese network for real time object tracking"
    - "high performance visual tracking with circular and structural operators"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-6 (induced by source paper "learning to update for object tracking with recurrent meta learner"):
  with members:
    - "good features to correlate for visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "re3 real time recurrent regression networks for visual tracking of generic objects"
    - "hierarchical attentive recurrent tracking"
    - "learning spatial aware regressions for visual tracking"
    - "youtube boundingboxes a large high precision human annotated data set for object detection in video"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "crest convolutional residual learning for visual tracking"

Connected through hyperedge HE-7 (induced by source paper "meta tracker fast and robust online adaptation for visual object trackers"):
  with members:
    - "end to end representation learning for correlation filter based tracking"
    - "re3 real time recurrent regression networks for visual tracking of generic objects"
    - "learned optimizers that scale and generalize"
    - "continuous adaptation via meta learning in nonstationary and competitive environments"
    - "meta sgd learning to learn quickly for few shot learning"
    - "crest convolutional residual learning for visual tracking"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "tracking as online decision making learning a policy from streaming videos with reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "the unmanned aerial vehicle benchmark object detection and tracking"):
  with members:
    - "end to end representation learning for correlation filter based tracking"
    - "simple online and realtime tracking with a deep association metric"
    - "drone based object counting by spatially regularized regional proposal network"
    - "ron reverse connection with objectness prior networks for object detection"
    - "parallel tracking and verifying a framework for real time and high accuracy visual tracking"
    - "okutama action an aerial view video dataset for concurrent human action detection"
    - "crest convolutional residual learning for visual tracking"
    - "eco efficient convolution operators for tracking"

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)