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: "actor action semantic segmentation with grouping process models"
  Abstract: Actor-action semantic segmentation made an important step toward advanced video understanding problems: what action is happening; who is performing the action; and where is the action in space-time. Current models for this problem are local, based on layered CRFs, and are unable to capture long-ranging interaction of video parts. We propose a new model that combines these local labeling CRFs with a hierarchical supervoxel decomposition. The supervoxels provide cues for possible groupings of nodes, at various scales, in the CRFs to encourage adaptive, high-order groups for more effective labeling. Our model is dynamic and continuously exchanges information during inference: the local CRFs influence what supervoxels in the hierarchy are active, and these active nodes influence the connectivity in the CRF; we hence call it a grouping process model. The experimental results on a recent large-scale video dataset show a large margin of 60% relative improvement over the state of the art, which demonstrates the effectiveness of the dynamic, bidirectional flow between labeling and grouping.

Connected through hyperedge HE-1 (induced by source paper "knowledge integration networks for action recognition"):
  with members:
    - "geometric pose affordance 3d human pose with scene constraints"
    - "eco efficient convolutional network for online video understanding"
    - "temporal bilinear networks for video action recognition"
    - "stnet local and global spatial temporal modeling for action recognition"
    - "actor action semantic segmentation with region masks"
    - "videos as space time region graphs"
    - "slowfast networks for video recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-2 (induced by source paper "hallucinet ing spatiotemporal representations using 2d cnn"):
  with members:
    - "design light weight 3d convolutional networks for video recognition temporal residual fully separable block and fast algorithm"
    - "what and how well you performed a multitask learning approach to action quality assessment"
    - "efficient video classification using fewer frames"
    - "temporal sequence distillation towards few frame action recognition in videos"
    - "motion feature network fixed motion filter for action recognition"
    - "rgb d based action recognition with light weight 3d convolutional networks"
    - "the pros and cons rank aware temporal attention for skill determination in long videos"
    - "slowfast networks for video recognition"

Connected through hyperedge HE-3 (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"
    - "remotenet efficient relevant motion event detection for large scale home surveillance videos"
    - "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-4 (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"
    - "remotenet efficient relevant motion event detection for large scale home surveillance videos"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"

Connected through hyperedge HE-5 (induced by source paper "actor and action video segmentation from a sentence"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "person search with natural language description"
    - "temporal action detection with structured segment networks"
    - "localizing actions from video labels and pseudo annotations"
    - "the kinetics human action video dataset"
    - "tall temporal activity localization via language query"
    - "spatio temporal person retrieval via natural language queries"
    - "spatial aware object embeddings for zero shot localization and classification of actions"

Connected through hyperedge HE-6 (induced by source paper "actor action semantic segmentation with region masks"):
  with members:
    - "masklab instance segmentation by refining object detection with semantic and direction features"
    - "mask r cnn"
    - "std2p rgbd semantic segmentation using spatio temporal data driven pooling"
    - "learning video object segmentation from static images"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "clockwork convnets for video semantic segmentation"
    - "region based semantic segmentation with end to end training"
    - "learning to segment object candidates"

Connected through hyperedge HE-7 (induced by source paper "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"):
  with members:
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "spot on action localization from pointly supervised proposals"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "segmental spatiotemporal cnns for fine grained action segmentation"
    - "much ado about time exhaustive annotation of temporal data"

Connected through hyperedge HE-8 (induced by source paper "exploring temporal preservation networks for precise temporal action localization"):
  with members:
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "review of action recognition and detection methods"
    - "temporal segment networks towards good practices for deep action recognition"
    - "untrimmed video classification for activity detection submission to activitynet challenge"
    - "convolutional two stream network fusion for video action recognition"
    - "spatiotemporal residual networks for video action recognition"

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)