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: "discrimnet semi supervised action recognition from videos using generative adversarial networks"
  Abstract: We propose an action recognition framework using Gen- erative Adversarial Networks. Our model involves train- ing a deep convolutional generative adversarial network (DCGAN) using a large video activity dataset without la- bel information. Then we use the trained discriminator from the GAN model as an unsupervised pre-training step and fine-tune the trained discriminator model on a labeled dataset to recognize human activities. We determine good network architectural and hyperparameter settings for us- ing the discriminator from DCGAN as a trained model to learn useful representations for action recognition. Our semi-supervised framework using only appearance infor- mation achieves superior or comparable performance to the current state-of-the-art semi-supervised action recog- nition methods on two challenging video activity datasets: UCF101 and HMDB51.

Connected through hyperedge HE-1 (induced by source paper "videossl semi supervised learning for video classification"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "only time can tell discovering temporal data for temporal modeling"
    - "distinit learning video representations without a single labeled video"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "learning with privileged information via adversarial discriminative modality distillation"
    - "video representation learning by dense predictive coding"
    - "semi supervised convolutional neural networks for human activity recognition"
    - "parallel separable 3d convolution for video and volumetric data understanding"

Connected through hyperedge HE-2 (induced by source paper "predicting the future a jointly learnt model for action anticipation"):
  with members:
    - "coupled generative adversarial network for continuous fine grained action segmentation"
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "deep cosine metric learning for person re identification"
    - "convolutional sequence to sequence model for human dynamics"
    - "multi level sequence gan for group activity recognition"
    - "action anticipation by predicting future dynamic images"
    - "encouraging lstms to anticipate actions very early"
    - "visual forecasting by imitating dynamics in natural sequences"

Connected through hyperedge HE-3 (induced by source paper "fine grained action segmentation using the semi supervised action gan"):
  with members:
    - "robust visual tracking using multi frame multi feature joint modeling"
    - "coupled generative adversarial network for continuous fine grained action segmentation"
    - "tracking by prediction a deep generative model for mutli person localisation and tracking"
    - "multi level sequence gan for group activity recognition"
    - "good semi supervised learning that requires a bad gan"
    - "tricornet a hybrid temporal convolutional and recurrent network for video action segmentation"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "semi supervised learning with context conditional generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "take an emotion walk perceiving emotions from gaits using hierarchical attention pooling and affective mapping"):
  with members:
    - "context aware emotion recognition networks"
    - "step spatial temporal graph convolutional networks for emotion perception from gaits"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "predicting 3d human dynamics from video"
    - "identifying emotions from walking using affective and deep features"
    - "a comparative review of recent kinect based action recognition algorithms"
    - "pose guided human video generation"
    - "quaternet a quaternion based recurrent model for human motion"

Connected through hyperedge HE-5 (induced by source paper "rethinking monocular depth estimation with adversarial training"):
  with members:
    - "unsupervised adversarial depth estimation using cycled generative networks"
    - "cross view image synthesis using conditional gans"
    - "deep ordinal regression network for monocular depth estimation"
    - "design design inspiration from generative networks"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "spectral normalization for generative adversarial networks"
    - "monocular depth estimation using multi scale continuous crfs as sequential deep networks"
    - "structured attention guided convolutional neural fields for monocular depth estimation"

Connected through hyperedge HE-6 (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"
    - "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"
    - "pixel level domain transfer"

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)