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: "an end to end generative framework for video segmentation and recognition"
  Abstract: We describe an end-to-end generative approach for the segmentation and recognition of human activities. In this approach, a visual representation based on reduced Fisher Vectors is combined with a structured temporal model for recognition. We show that the statistical properties of Fisher Vectors make them an especially suitable front-end for generative models such as Gaussian mixtures. The system is evaluated for both the recognition of complex activities as well as their parsing into action units. Using a variety of video datasets ranging from human cooking activities to animal behaviors, our experiments demonstrate that the resulting architecture outperforms state-of-the-art approaches for larger datasets, i.e. when sufficient amount of data is available for training structured generative models.

Connected through hyperedge HE-1 (induced by source paper "a hybrid rnn hmm approach for weakly supervised temporal action segmentation"):
  with members:
    - "weakly supervised action segmentation with iterative soft boundary assignment"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "weakly supervised action localization by sparse temporal pooling network"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "the kinetics human action video dataset"
    - "connectionist temporal modeling for weakly supervised action labeling"

Connected through hyperedge HE-2 (induced by source paper "joint visual temporal embedding for unsupervised learning of actions in untrimmed sequences"):
  with members:
    - "a hybrid rnn hmm approach for weakly supervised temporal action segmentation"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "weakly supervised energy based learning for action segmentation"
    - "unsupervised learning of action classes with continuous temporal embedding"
    - "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
    - "unsupervised learning and segmentation of complex activities from video"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "a perceptual prediction framework for self supervised event segmentation"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised action segmentation using mutual consistency"):
  with members:
    - "ms tcn multi stage temporal convolutional network for action segmentation"
    - "weakly supervised energy based learning for action segmentation"
    - "large scale holistic video understanding"
    - "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
    - "group normalization"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "spatio temporal channel correlation networks for action classification"
    - "slowfast networks for video recognition"

Connected through hyperedge HE-4 (induced by source paper "coupled generative adversarial network for continuous fine grained action segmentation"):
  with members:
    - "stabilizing gan training with multiple random projections"
    - "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"
    - "stabilizing training of generative adversarial networks through regularization"
    - "tricornet a hybrid temporal convolutional and recurrent network for video action segmentation"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "unsupervised learning of action classes with continuous temporal embedding"):
  with members:
    - "unsupervised learning and segmentation of complex activities from video"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "spatio temporal channel correlation networks for action classification"
    - "weakly supervised action segmentation with iterative soft boundary assignment"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "generalized rank pooling for activity recognition"
    - "unsupervised video understanding by reconciliation of posture similarities"

Connected through hyperedge HE-6 (induced by source paper "ddlstm dual domain lstm for cross dataset action recognition"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "autodial automatic domain alignment layers"
    - "lstm a search space odyssey"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "deep hashing network for unsupervised domain adaptation"

Connected through hyperedge HE-7 (induced by source paper "weakly supervised energy based learning for action segmentation"):
  with members:
    - "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "weakly supervised action segmentation with iterative soft boundary assignment"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "action sets weakly supervised action segmentation without ordering constraints"
    - "tricornet a hybrid temporal convolutional and recurrent network for video action segmentation"
    - "connectionist temporal modeling for weakly supervised action labeling"
    - "weakly supervised learning of actions from transcripts"

Connected through hyperedge HE-8 (induced by source paper "ms tcn multi stage temporal convolutional network for action segmentation"):
  with members:
    - "weakly supervised action segmentation with iterative soft boundary assignment"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "connectionist temporal modeling for weakly supervised action labeling"
    - "weakly supervised learning of actions from transcripts"
    - "temporal convolutional networks for action segmentation and 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)