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: "actionness estimation using hybrid fully convolutional networks"
  Abstract: Actionness was introduced to quantify the likelihood of containing a generic action instance at a specific location. Accurate and efficient estimation of actionness is important in video analysis and may benefit other relevant tasks such as action recognition and action detection. This paper presents a new deep architecture for actionness estimation, called hybrid fully convolutional network (H-FCN), which is composed of appearance FCN (A-FCN) and motion FCN (M-FCN). These two FCNs leverage the strong capacity of deep models to estimate actionness maps from the perspectives of static appearance and dynamic motion, respectively. In addition, the fully convolutional nature of H-FCN allows it to efficiently process videos with arbitrary sizes. Experiments are conducted on the challenging datasets of Stanford40, UCF Sports, and JHMDB to verify the effectiveness of H-FCN on actionness estimation, which demonstrate that our method achieves superior performance to previous ones. Moreover, we apply the estimated actionness maps on action proposal generation and action detection. Our actionness maps advance the current state-of-the-art performance of these tasks substantially.

Connected through hyperedge HE-1 (induced by source paper "actions as moving points"):
  with members:
    - "tacnet transition aware context network for spatio temporal action detection"
    - "centernet keypoint triplets for object detection"
    - "step spatio temporal progressive learning for video action detection"
    - "fcos fully convolutional one stage object detection"
    - "objects as points"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "simple baselines for human pose estimation and tracking"

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 "improving action localization by progressive cross stream cooperation"):
  with members:
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "crowd counting using deep recurrent spatial aware network"
    - "modeling spatio temporal human track structure for action localization"
    - "actor centric relation network"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "action tubelet detector for spatio temporal action localization"
    - "tube convolutional neural network t cnn for action detection in videos"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"

Connected through hyperedge HE-4 (induced by source paper "video summarization via actionness ranking"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "temporal action detection with structured segment networks"
    - "video summarization with attention based encoder decoder networks"
    - "turn tap temporal unit regression network for temporal action proposals"
    - "searching action proposals via spatial actionness estimation and temporal path inference and tracking"
    - "temporal activity detection in untrimmed videos with recurrent neural networks"
    - "summary transfer exemplar based subset selection for video summarization"
    - "video summarization with long short term memory"

Connected through hyperedge HE-5 (induced by source paper "deep rnn framework for visual sequential applications"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "human action adverb recognition adha dataset and a three stream hybrid model"
    - "srda generating instance segmentation annotation via scanning reasoning and domain adaptation"
    - "yolov3 an incremental improvement"
    - "crowdpose efficient crowded scenes pose estimation and a new benchmark"
    - "recurrent residual module for fast inference in videos"
    - "decomposing motion and content for natural video sequence prediction"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-6 (induced by source paper "s3d single shot multi span detector via fully 3d convolutional networks"):
  with members:
    - "deep exemplar based colorization"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"
    - "temporal action detection with structured segment networks"
    - "temporal context network for activity localization in videos"
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "deep reinforcement learning for visual object tracking in videos"
    - "stylebank an explicit representation for neural image style transfer"

Connected through hyperedge HE-7 (induced by source paper "temporal action detection with structured segment networks"):
  with members:
    - "s3d single shot multi span detector via fully 3d convolutional networks"
    - "ctap complementary temporal action proposal generation"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "video object segmentation with joint re identification and attention aware mask propagation"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"
    - "temporal segment networks for action recognition in videos"

Connected through hyperedge HE-8 (induced by source paper "weakly supervised action localization by sparse temporal pooling network"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "temporal action detection with structured segment networks"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "temporal action localization by structured maximal sums"

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)