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: "memory augmented attention modelling for videos"
  Abstract: Recent works on neural architectures have demonstrated the utility of attention mechanisms for a wide variety of tasks. Attention models used for problems such as image captioning typically depend on the image under consideration, as well as the previous sequence of words that come before the word currently being generated. While these types of models have produced impressive results, they are not able to model the higher-order interactions involved in problems such as video description/captioning, where the relationship between parts of the video and the concepts being depicted is complex. Motivated by these observations, we propose a novel memory-based attention model for video description. Our model utilizes memories of past attention when reasoning about where to attend to in the current time step, similar to the central executive system proposed in human cognition. This allows the model to not only reason about local attention more effectively, it allows it to consider the entire sequence of video frames while generating each word. Evaluation on the challenging and popular MSVD and Charades datasets show that the proposed architecture outperforms all previously proposed methods and leads to a new state of the art results in the video description.

Connected through hyperedge HE-1 (induced by source paper "human action sequence classification"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "forecasting future action sequences with neural memory networks"
    - "streamlined dense video captioning"
    - "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
    - "weakly supervised gaussian networks for action detection"
    - "end to end dense video captioning with masked transformer"
    - "jointly localizing and describing events for dense video captioning"
    - "timeception for complex action recognition"

Connected through hyperedge HE-2 (induced by source paper "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"):
  with members:
    - "video description a survey of methods datasets and evaluation metrics"
    - "less is more picking informative frames for video captioning"
    - "reconstruction network for video captioning"
    - "hierarchical boundary aware neural encoder for video captioning"
    - "video captioning with transferred semantic attributes"
    - "recurrent memory addressing for describing videos"
    - "semantic compositional networks for visual captioning"
    - "inception v4 inception resnet and the impact of residual connections on learning"

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)