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: "fanet quality aware feature aggregation network for rgb t tracking"
  Abstract: This paper investigates how to perform robust visual tracking in adverse and challenging conditions using complementary visual and thermal infrared data (RGB-T tracking). We propose a novel deep network architecture "quality-aware Feature Aggregation Network (FANet)" to achieve quality-aware aggregations of both hierarchical features and multimodal information for robust online RGB-T tracking. Unlike existing works that directly concatenate hierarchical deep features, our FANet learns the layer weights to adaptively aggregate them to handle the challenge of significant appearance changes caused by deformation, abrupt motion, background clutter and occlusion within each modality. Moreover, we employ the operations of max pooling, interpolation upsampling and convolution to transform these hierarchical and multi-resolution features into a uniform space at the same resolution for more effective feature aggregation. In different modalities, we elaborately design a multimodal aggregation sub-network to integrate all modalities collaboratively based on the predicted reliability degrees. Extensive experiments on large-scale benchmark datasets demonstrate that our FANet significantly outperforms other state-of-the-art RGB-T tracking methods.

Connected through hyperedge HE-1 (induced by source paper "cross modality person re identification with shared specific feature transfer"):
  with members:
    - "interaction and aggregation network for person re identification"
    - "hpiln a feature learning framework for cross modality person re identification"
    - "learning disentangled representation for robust person re identification"
    - "a new benchmark and approach for fine grained cross media retrieval"
    - "joint discriminative and generative learning for person re identification"
    - "beyond intra modality discrepancy a comprehensive survey of heterogeneous person re identification"
    - "mtfh a matrix tri factorization hashing framework for efficient cross modal retrieval"
    - "attend to the difference cross modality person re identification via contrastive correlation"

Connected through hyperedge HE-2 (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"
    - "dense feature aggregation and pruning for rgbt tracking"
    - "deeper and wider siamese networks for real time visual tracking"
    - "real time mdnet"
    - "rgb t object tracking benchmark and baseline"
    - "end to end representation learning for correlation filter based tracking"

Connected through hyperedge HE-3 (induced by source paper "multi adapter rgbt tracking"):
  with members:
    - "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"
    - "deep attentive tracking via reciprocative learning"
    - "learning cross modal deep representations for robust pedestrian detection"
    - "learning multiple visual domains with residual adapters"
    - "eco efficient convolution operators for tracking"

Connected through hyperedge HE-4 (induced by source paper "learning target oriented dual attention for robust rgb t tracking"):
  with members:
    - "real time mdnet"
    - "rgb t object tracking benchmark and baseline"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "describe and attend to track learning natural language guided structural representation and visual attention for object tracking"
    - "deep attentive tracking via reciprocative learning"
    - "learning spatially regularized correlation filters for visual tracking"
    - "learning multi domain convolutional neural networks for visual 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)