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: "dense feature aggregation and pruning for rgbt tracking"
  Abstract: How to perform effective information fusion of different modalities is a core factor in boosting the performance of RGBT tracking. This paper presents a novel deep fusion algorithm based on the representations from an end-to-end trained convolutional neural network. To deploy the complementarity of features of all layers, we propose a recursive strategy to densely aggregate these features that yield robust representations of target objects in each modality. In different modalities, we propose to prune the densely aggregated features of all modalities in a collaborative way. In a specific, we employ the operations of global average pooling and weighted random selection to perform channel scoring and selection, which could remove redundant and noisy features to achieve more robust feature representation. Experimental results on two RGBT tracking benchmark datasets suggest that our tracker achieves clear state-of-the-art against other RGB and RGBT tracking methods.

Connected through hyperedge HE-1 (induced by source paper "m 5 l multi modal multi margin metric learning for rgbt tracking"):
  with members:
    - "multi modal fusion for end to end rgb t tracking"
    - "ranked list loss for deep metric learning"
    - "multi adapter rgbt tracking"
    - "deeper and wider siamese networks for real time visual tracking"
    - "real time mdnet"
    - "fanet quality aware feature aggregation network for rgb t tracking"
    - "rgb t object tracking benchmark and baseline"
    - "end to end representation learning for correlation filter based tracking"

Connected through hyperedge HE-2 (induced by source paper "improved hard example mining by discovering attribute based hard person identity"):
  with members:
    - "fmt fusing multi task convolutional neural network for person search"
    - "pedestrian attribute recognition a survey"
    - "auto reid searching for a part aware convnet for person re identification"
    - "improving person re identification by attribute and identity learning"
    - "in defense of the triplet loss for person re identification"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "image image domain adaptation with preserved self similarity and domain dissimilarity for person re identification"

Connected through hyperedge HE-3 (induced by source paper "fanet quality aware feature aggregation network for robust rgb t tracking"):
  with members:
    - "multi adapter rgbt tracking"
    - "segmenting objects in day and night edge conditioned cnn for thermal image semantic segmentation"
    - "learning target oriented dual attention for robust rgb t tracking"
    - "selective kernel networks"
    - "deeper and wider siamese networks for real time visual tracking"
    - "real time mdnet"
    - "rgb t object tracking benchmark and baseline"
    - "meta tracker fast and robust online adaptation for visual object trackers"

Connected through hyperedge HE-4 (induced by source paper "fanet quality aware feature aggregation network for rgb t tracking"):
  with members:
    - "multi adapter rgbt tracking"
    - "segmenting objects in day and night edge conditioned cnn for thermal image semantic segmentation"
    - "learning target oriented dual attention for robust rgb t tracking"
    - "selective kernel networks"
    - "deeper and wider siamese networks for real time visual tracking"
    - "real time mdnet"
    - "rgb t object tracking benchmark and baseline"
    - "meta tracker fast and robust online adaptation for visual object trackers"

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)