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: "remotenet efficient relevant motion event detection for large scale home surveillance videos"
  Abstract: This paper addresses the problem of detecting relevant motion caused by objects of interest (e.g., person and vehicles) in large scale home surveillance videos. The traditional method usually consists of two separate steps, i.e., detecting moving objects with background subtraction running on the camera, and filtering out nuisance motion events (e.g., trees, cloud, shadow, rain/snow, flag) with deep learning based object detection and tracking running on cloud. The method is extremely slow and therefore not cost effective, and does not fully leverage the spatial-temporal redundancies with a pre-trained off-the-shelf object detector. To dramatically speedup relevant motion event detection and improve its performance, we propose a novel network for relevant motion event detection, ReMotENet, which is a unified, end-to-end data-driven method using spatial-temporal attention-based 3D ConvNets to jointly model the appearance and motion of objects-of-interest in a video. ReMotENet parses an entire video clip in one forward pass of a neural network to achieve significant speedup. Meanwhile, it exploits the properties of home surveillance videos, e.g., relevant motion is sparse both spatially and temporally, and enhances 3D ConvNets with a spatial-temporal attention model and reference-frame subtraction to encourage the network to focus on the relevant moving objects. Experiments demonstrate that our method can achieve comparable or event better performance than the object detection based method but with three to four orders of magnitude speedup (up to 20k times) on GPU devices. Our network is efficient, compact and light-weight. It can detect relevant motion on a 15s surveillance video clip within 4-8 milliseconds on a GPU and a fraction of second (0.17-0.39) on a CPU with a model size of less than 1MB.

Connected through hyperedge HE-1 (induced by source paper "representing videos based on scene layouts for recognizing agent in place actions"):
  with members:
    - "actor centric relation network"
    - "dynamic temporal pyramid network a closer look at multi scale modeling for activity detection"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"
    - "c wsl count guided weakly supervised localization"
    - "attend and interact higher order object interactions for video understanding"

Connected through hyperedge HE-2 (induced by source paper "layout induced video representation for recognizing agent in place actions"):
  with members:
    - "actor centric relation network"
    - "learning human object interactions by graph parsing neural networks"
    - "dynamic temporal pyramid network a closer look at multi scale modeling for activity detection"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"
    - "c wsl count guided weakly supervised localization"

Connected through hyperedge HE-3 (induced by source paper "nisp pruning networks using neuron importance score propagation"):
  with members:
    - "learning rich features for image manipulation detection"
    - "viton an image based virtual try on network"
    - "c wsl count guided weakly supervised localization"
    - "blockdrop dynamic inference paths in residual networks"
    - "thinet a filter level pruning method for deep neural network compression"
    - "visual relationship detection with internal and external linguistic knowledge distillation"
    - "deep regionlets for object detection"
    - "dynamic zoom in network for fast object detection in large images"

Connected through hyperedge HE-4 (induced by source paper "dynamic zoom in network for fast object detection in large images"):
  with members:
    - "nisp pruning networks using neuron importance score propagation"
    - "c wsl count guided weakly supervised localization"
    - "visual relationship detection with internal and external linguistic knowledge distillation"
    - "mask r cnn"
    - "generating holistic 3d scene abstractions for text based image retrieval"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "the role of context selection in object detection"

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)