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 "structural rnn deep learning on spatio temporal graphs"):
  Abstract: Deep Recurrent Neural Network architectures, though remarkably capable at modeling sequences, lack an intuitive high-level spatio-temporal structure. That is while many problems in computer vision inherently have an underlying high-level structure and can benefit from it. Spatio-temporal graphs are a popular tool for imposing such high-level intuitions in the formulation of real world problems. In this paper, we propose an approach for combining the power of high-level spatio-temporal graphs and sequence learning success of Recurrent Neural Networks~(RNNs). We develop a scalable method for casting an arbitrary spatio-temporal graph as a rich RNN mixture that is feedforward, fully differentiable, and jointly trainable. The proposed method is generic and principled as it can be used for transforming any spatio-temporal graph through employing a certain set of well defined steps. The evaluations of the proposed approach on a diverse set of problems, ranging from modeling human motion to object interactions, shows improvement over the state-of-the-art with a large margin. We expect this method to empower new approaches to problem formulation through high-level spatio-temporal graphs and Recurrent Neural Networks.

Includes members:
  Member "semantic image segmentation via deep parsing network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "car that knows before you do anticipating maneuvers via learning temporal driving models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards accurate vehicle behaviour classification with multi relational graph convolutional networks")
  Member "unsupervised learning of video representations using lstms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "conditional random fields as recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "real time high performance semantic image segmentation of urban street scenes")
  Member "recurrent network models for human dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "fast r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "visualizing and understanding recurrent networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "visual summary of value level feature attribution in prediction classes with recurrent neural networks")
  Member "fully connected deep structured networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gated scnn gated shape cnns for semantic segmentation")

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)