human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "multi level sequence gan for group activity recognition"):
  Abstract: We propose a novel semi-supervised, Multi-Level Sequential Generative Adversarial Network (MLS-GAN) architecture for group activity recognition. In contrast to previous works which utilise manually annotated individual human action predictions, we allow the models to learn it's own internal representations to discover pertinent sub-activities that aid the final group activity recognition task. The generator is fed with person-level and scene-level features that are mapped temporally through LSTM networks. Action-based feature fusion is performed through novel gated fusion units that are able to consider long-term dependencies, exploring the relationships among all individual actions, to learn an intermediate representation or `action code' for the current group activity. The network achieves its semi-supervised behaviour by allowing it to perform group action classification together with the adversarial real/fake validation. We perform extensive evaluations on different architectural variants to demonstrate the importance of the proposed architecture. Furthermore, we show that utilising both person-level and scene-level features facilitates the group activity prediction better than using only person-level features. Our proposed architecture outperforms current state-of-the-art results for sports and pedestrian based classification tasks on Volleyball and Collective Activity datasets, showing it's flexible nature for effective learning of group activities.

Includes members:
  Member "tracking by prediction a deep generative model for mutli person localisation and tracking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multiple object tracking by flowing and fusing")
  Member "discrimnet semi supervised action recognition from videos using generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "videossl semi supervised learning for video classification")
  Member "task specific visual saliency prediction with memory augmented conditional generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exploiting human social cognition for the detection of fake and fraudulent faces via memory networks")
  Member "structural recurrent neural network srnn for group activity analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "progressive relation learning for group activity recognition")
  Member "infogail interpretable imitation learning from visual demonstrations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning")
  Member "semi supervised learning with context conditional generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey towards federated semi supervised learning")
  Member "two stream lstm a deep fusion framework for human action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "automatic video object segmentation via motion appearance stream fusion and instance aware segmentation")
  Member "cern confidence energy recurrent network for group activity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")

Question: Please tell me which class the source paper 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)