human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "bobby2 buffer based robust high speed object tracking"):
  Abstract: In this work, a novel high-speed single object tracker that is robust against non-semantic distractor exemplars is introduced; dubbed BOBBY2. It incorporates a novel exemplar buffer module that sparsely caches the target's appearance across time, enabling it to adapt to potential target deformation. As for training, an augmented ImageNet-VID dataset was used in conjunction with the one cycle policy, enabling it to reach convergence with less than 2 epoch worth of data. For validation, the model was benchmarked on the GOT-10k dataset and on an additional small, albeit challenging custom UAV dataset collected with the TU-3 UAV. We demonstrate that the exemplar buffer is capable of providing redundancies in case of unintended target drifts, a desirable trait in any middle to long term tracking. Even when the buffer is predominantly filled with distractors instead of valid exemplars, BOBBY2 is capable of maintaining a near-optimal level of accuracy. BOBBY2 manages to achieve a very competitive result on the GOT-10k dataset and to a lesser degree on the challenging custom TU-3 dataset, without fine-tuning, demonstrating its generalizability. In terms of speed, BOBBY2 utilizes a stripped down AlexNet as feature extractor with 63% less parameters than a vanilla AlexNet, thus being able to run at a competitive 85 FPS.

Includes members:
  Member "got 10k a large high diversity benchmark for generic object tracking in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "siamese box adaptive network for visual tracking")
  Member "distractor aware siamese networks for visual object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  Member "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning architectures for binary networks")
  Member "enkcf ensemble of kernelized correlation filters for high speed object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient object detection in large images using deep reinforcement learning")
  Member "fast online object tracking and segmentation a unifying approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "end to end representation learning for correlation filter based tracking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  Member "vision based real time aerial object localization and tracking for uav sensing system":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reconstruction of 3d flight trajectories from ad hoc camera networks")
  Member "super convergence very fast training of residual networks using large learning rates":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "rethinking the hyperparameters for fine tuning")

Question: Please tell me which class the source paper 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)