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: "efficient action detection in untrimmed videos via multi task learning"
  Abstract: This paper studies the joint learning of action recognition and temporal localization in long, untrimmed videos. We employ a multi-task learning framework that performs the three highly related steps of action proposal, action recognition, and action localization refinement in parallel instead of the standard sequential pipeline that performs the steps in order. We develop a novel temporal actionness regression module that estimates what proportion of a clip contains action. We use it for temporal localization but it could have other applications like video retrieval, surveillance, summarization, etc. We also introduce random shear augmentation during training to simulate viewpoint change. We evaluate our framework on three popular video benchmarks. Results demonstrate that our joint model is efficient in terms of storage and computation in that we do not need to compute and cache dense trajectory features, and that it is several times faster than its sequential ConvNets counterpart. Yet, despite being more efficient, it outperforms state-of-the-art methods with respect to accuracy.

Connected through hyperedge HE-1 (induced by source paper "skeleton based online action prediction using scale selection network"):
  with members:
    - "leveraging structural context models and ranking score fusion for human interaction prediction"
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "skeleton boxes solving skeleton based action detection with a single deep convolutional neural network"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "temporal action detection with structured segment networks"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "cascaded boundary regression for temporal action detection"
    - "temporal context network for activity localization in videos"

Connected through hyperedge HE-2 (induced by source paper "delving into 3d action anticipation from streaming videos"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "on the integration of optical flow and action recognition"
    - "temporal action detection with structured segment networks"
    - "encouraging lstms to anticipate actions very early"

Connected through hyperedge HE-3 (induced by source paper "step by step erasion one by one collection a weakly supervised temporal action detector"):
  with members:
    - "monocular depth estimation using multi scale continuous crfs as sequential deep networks"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"
    - "temporal action detection with structured segment networks"
    - "temporal segment networks for action recognition in videos"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "untrimmednets for weakly supervised action recognition and detection"

Connected through hyperedge HE-4 (induced by source paper "temporal action detection by joint identification verification"):
  with members:
    - "a discriminatively learned cnn embedding for person reidentification"
    - "single shot temporal action detection"
    - "temporal action detection with structured segment networks"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "a pursuit of temporal accuracy in general activity detection"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "gated siamese convolutional neural network architecture for human re identification"

Connected through hyperedge HE-5 (induced by source paper "single shot temporal action detection"):
  with members:
    - "deep quantization encoding convolutional activations with deep generative model"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "spot on action localization from pointly supervised proposals"
    - "untrimmed video classification for activity detection submission to activitynet challenge"
    - "convolutional two stream network fusion for video action recognition"
    - "temporal convolutional networks a unified approach to action segmentation"

Connected through hyperedge HE-6 (induced by source paper "a self adaptive proposal model for temporal action detection based on reinforcement learning"):
  with members:
    - "turn tap temporal unit regression network for temporal action proposals"
    - "tree structured reinforcement learning for sequential object localization"
    - "a pursuit of temporal accuracy in general activity detection"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "hierarchical object detection with deep reinforcement learning"
    - "end to end learning of action detection from frame glimpses in videos"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "active object localization with deep reinforcement learning"

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)