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: "deep structured models for group activity recognition"
  Abstract: This paper presents a deep neural-network-based hierarchical graphical model for individual and group activity recognition in surveillance scenes. Deep networks are used to recognize the actions of individual people in a scene. Next, a neural-network-based hierarchical graphical model refines the predicted labels for each class by considering dependencies between the classes. This refinement step mimics a message-passing step similar to inference in a probabilistic graphical model. We show that this approach can be effective in group activity recognition, with the deep graphical model improving recognition rates over baseline methods.

Connected through hyperedge HE-1 (induced by source paper "captioning near future activity sequences"):
  with members:
    - "move forward and tell a progressive generator of video descriptions"
    - "when will you do what anticipating temporal occurrences of activities"
    - "dense captioning events in videos"
    - "estimating human intent for physical human robot co manipulation"
    - "towards automatic learning of procedures from web instructional videos"
    - "recurrent topic transition gan for visual paragraph generation"
    - "spice semantic propositional image caption evaluation"
    - "first person activity forecasting with online inverse reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "convolutional relational machine for group activity recognition"):
  with members:
    - "towards vision based smart hospitals a system for tracking and monitoring hand hygiene compliance"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "unsupervised learning of long term motion dynamics for videos"
    - "cern confidence energy recurrent network for group activity recognition"
    - "convolutional pose machines"
    - "social scene understanding end to end multi person action localization and collective activity recognition"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"

Connected through hyperedge HE-3 (induced by source paper "joint attention in driver pedestrian interaction from theory to practice"):
  with members:
    - "when vehicles see pedestrians with phones a multi cue framework for recognizing phone based activities of pedestrians"
    - "video salient object detection via fully convolutional networks"
    - "agreeing to cross how drivers and pedestrians communicate"
    - "citypersons a diverse dataset for pedestrian detection"
    - "integrating three mechanisms of visual attention for active visual search"
    - "learning where to attend like a human driver"
    - "real time human motion capture with multiple depth cameras"
    - "hcp a flexible cnn framework for multi label image classification"

Connected through hyperedge HE-4 (induced by source paper "multi level sequence gan for group activity recognition"):
  with members:
    - "tracking by prediction a deep generative model for mutli person localisation and tracking"
    - "discrimnet semi supervised action recognition from videos using generative adversarial networks"
    - "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"
    - "structural recurrent neural network srnn for group activity analysis"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "semi supervised learning with context conditional generative adversarial networks"
    - "two stream lstm a deep fusion framework for human action recognition"
    - "cern confidence energy recurrent network for group activity recognition"

Connected through hyperedge HE-5 (induced by source paper "structural recurrent neural network srnn for group activity analysis"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "cern confidence energy recurrent network for group activity recognition"
    - "hierarchical deep temporal models for group activity recognition"
    - "social scene understanding end to end multi person action localization and collective activity recognition"
    - "structure inference machines recurrent neural networks for analyzing relations in group activity recognition"
    - "a hierarchical deep temporal model for group activity recognition"
    - "structural rnn deep learning on spatio temporal graphs"
    - "detecting events and key actors in multi person videos"

Connected through hyperedge HE-6 (induced by source paper "hierarchical long short term concurrent memory for human interaction recognition"):
  with members:
    - "structural recurrent neural network srnn for group activity analysis"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "spatial structural and temporal feature learning for human interaction prediction"
    - "visual recognition by counting instances a multi instance cardinality potential kernel"
    - "structure inference machines recurrent neural networks for analyzing relations in group activity recognition"
    - "a hierarchical deep temporal model for group activity recognition"
    - "fast r cnn"
    - "differential recurrent neural networks for action recognition"

Connected through hyperedge HE-7 (induced by source paper "who did what at where and when simultaneous multi person tracking and activity recognition"):
  with members:
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "poi multiple object tracking with high performance detection and appearance feature"
    - "social scene understanding end to end multi person action localization and collective activity recognition"
    - "visual recognition by counting instances a multi instance cardinality potential kernel"
    - "structure inference machines recurrent neural networks for analyzing relations in group activity recognition"
    - "a hierarchical deep temporal model for group activity recognition"

Connected through hyperedge HE-8 (induced by source paper "learning person trajectory representations for team activity analysis"):
  with members:
    - "spatio temporal analysis of team sports"
    - "human action recognition using factorized spatio temporal convolutional networks"
    - "structure inference machines recurrent neural networks for analyzing relations in group activity recognition"
    - "a hierarchical deep temporal model for group activity recognition"
    - "fully connected deep structured networks"
    - "detecting events and key actors in multi person videos"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "learning spatiotemporal features with 3d convolutional networks"

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)