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: "sequence level semantics aggregation for video object detection"
  Abstract: Video objection detection (VID) has been a rising research direction in recent years. A central issue of VID is the appearance degradation of video frames caused by fast motion. This problem is essentially ill-posed for a single frame. Therefore, aggregating features from other frames becomes a natural choice. Existing methods rely heavily on optical flow or recurrent neural networks for feature aggregation. However, these methods emphasize more on the temporally nearby frames. In this work, we argue that aggregating features in the full-sequence level will lead to more discriminative and robust features for video object detection. To achieve this goal, we devise a novel Sequence Level Semantics Aggregation (SELSA) module. We further demonstrate the close relationship between the proposed method and the classic spectral clustering method, providing a novel view for understanding the VID problem. We test the proposed method on the ImageNet VID and the EPIC KITCHENS dataset and achieve new state-of-the-art results. Our method does not need complicated postprocessing methods such as Seq-NMS or Tubelet rescoring, which keeps the pipeline simple and clean.

Connected through hyperedge HE-1 (induced by source paper "motion excited sampler video adversarial attack with sparked prior"):
  with members:
    - "sparse black box video attack with reinforcement learning"
    - "heuristic black box adversarial attacks on video recognition models"
    - "deep video inpainting"
    - "appending adversarial frames for universal video attack"
    - "black box adversarial attacks on video recognition models"
    - "adversarial attacks for optical flow based action recognition classifiers"
    - "sparse adversarial perturbations for videos"
    - "fast online object tracking and segmentation a unifying approach"

Connected through hyperedge HE-2 (induced by source paper "plug play convolutional regression tracker for video object detection"):
  with members:
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "deep high resolution representation learning for human pose estimation"
    - "relation distillation networks for video object detection"
    - "object detection in video with spatiotemporal sampling networks"
    - "optimizing video object detection via a scale time lattice"
    - "integrated object detection and tracking with tracklet conditioned detection"
    - "deformable convnets v2 more deformable better results"
    - "flow guided feature aggregation for video object detection"

Connected through hyperedge HE-3 (induced by source paper "long term temporal context for per camera object detection"):
  with members:
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "videobert a joint model for video and language representation learning"
    - "fcos fully convolutional one stage object detection"
    - "objects as points"
    - "scale mlperf 0 6 models on google tpu v3 pods"
    - "synthetic examples improve generalization for rare classes"
    - "vl bert pre training of generic visual linguistic representations"
    - "long term feature banks for detailed video understanding"

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)