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: "discriminative correlation filter tracker with channel and spatial reliability"
  Abstract: Short-term tracking is an open and challenging problem for which discriminative correlation filters (DCF) have shown excellent performance. We introduce the channel and spatial reliability concepts to DCF tracking and provide a learning algorithm for its efficient and seamless integration in the filter update and the tracking process. The spatial reliability map adjusts the filter support to the part of the object suitable for tracking. This both allows to enlarge the search region and improves tracking of non-rectangular objects. Reliability scores reflect channel-wise quality of the learned filters and are used as feature weighting coefficients in localization. Experimentally, with only two simple standard feature sets, HoGs and colornames, the novel CSR-DCF method--DCF with channel and spatial reliability--achieves state-of-the-art results on VOT 2016, VOT 2015 and OTB100. The CSR-DCF runs close to real-time on a CPU.

Connected through hyperedge HE-1 (induced by source paper "tracking of micro unmanned aerial vehicles a comparative study"):
  with members:
    - "detect or track towards cost effective video object detection tracking"
    - "learning to track at 100 fps with deep regression networks"
    - "high speed tracking with kernelized correlation filters"
    - "outdoor flocking and formation flight with autonomous aerial robots"

Connected through hyperedge HE-2 (induced by source paper "interaction dataset an international adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps"):
  with members:
    - "generic prediction architecture considering both rational and irrational driving behaviors"
    - "human motion trajectory prediction a survey"
    - "top view trajectories a pedestrian dataset of vehicle crowd interaction from controlled experiments and crowded campus"
    - "interpretable modelling of driving behaviors in interactive driving scenarios based on cumulative prospect theory"
    - "an lstm network for highway trajectory prediction"
    - "deep imitative models for flexible inference planning and control"
    - "towards a fatality aware benchmark of probabilistic reaction prediction in highly interactive driving scenarios"
    - "hg dagger interactive imitation learning with human experts"

Connected through hyperedge HE-3 (induced by source paper "top view trajectories a pedestrian dataset of vehicle crowd interaction from controlled experiments and crowded campus"):
  with members:
    - "an evaluation of trajectory prediction approaches and notes on the trajnet benchmark"
    - "the highd dataset a drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems"
    - "a data driven model for interaction aware pedestrian motion prediction in object cluttered environments"

Connected through hyperedge HE-4 (induced by source paper "handcrafted and deep trackers recent visual object tracking approaches and trends"):
  with members:
    - "adaptive correlation filters with long term and short term memory for object tracking"
    - "vital visual tracking via adversarial learning"
    - "learning spatial temporal regularized correlation filters for visual tracking"
    - "dcfnet discriminant correlation filters network for visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "tracking the trackers an analysis of the state of the art in multiple object tracking"
    - "dual deep network for visual tracking"

Connected through hyperedge HE-5 (induced by source paper "atom accurate tracking by overlap maximization"):
  with members:
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "joint representation and truncated inference learning for correlation filter based tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "unveiling the power of deep tracking"
    - "acquisition of localization confidence for accurate object detection"
    - "correlation tracking via joint discrimination and reliability learning"
    - "towards a better match in siamese network based visual object tracker"

Connected through hyperedge HE-6 (induced by source paper "unveiling the power of deep tracking"):
  with members:
    - "a twofold siamese network for real time object tracking"
    - "vital visual tracking via adversarial learning"
    - "end to end representation learning for correlation filter based tracking"
    - "learning spatial aware regressions for visual tracking"
    - "deep lk for efficient adaptive object tracking"
    - "crest convolutional residual learning for visual tracking"
    - "need for speed a benchmark for higher frame rate object tracking"
    - "a novel performance evaluation methodology for single target trackers"

Connected through hyperedge HE-7 (induced by source paper "learning dynamic memory networks for object tracking"):
  with members:
    - "recurrent filter learning for visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "large margin object tracking with circulant feature maps"
    - "crest convolutional residual learning for visual tracking"
    - "one shot learning with memory augmented neural networks"
    - "siamese instance search for tracking"
    - "fully convolutional siamese networks for object tracking"

Connected through hyperedge HE-8 (induced by source paper "describe and attend to track learning natural language guided structural representation and visual attention for object tracking"):
  with members:
    - "real time mdnet"
    - "dynamic multimodal instance segmentation guided by natural language queries"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "relational inductive biases deep learning and graph networks"
    - "deep attentive tracking via reciprocative learning"
    - "learning dynamic memory networks for object tracking"
    - "attention is all you need"

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)