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: "learning latent sub events in activity videos using temporal attention filters"
  Abstract: In this paper, we newly introduce the concept of temporal attention filters, and describe how they can be used for human activity recognition from videos. Many high-level activities are often composed of multiple temporal parts (e.g., sub-events) with different duration/speed, and our objective is to make the model explicitly learn such temporal structure using multiple attention filters and benefit from them. Our temporal filters are designed to be fully differentiable, allowing end-of-end training of the temporal filters together with the underlying frame-based or segment-based convolutional neural network architectures. This paper presents an approach of learning a set of optimal static temporal attention filters to be shared across different videos, and extends this approach to dynamically adjust attention filters per testing video using recurrent long short-term memory networks (LSTMs). This allows our temporal attention filters to learn latent sub-events specific to each activity. We experimentally confirm that the proposed concept of temporal attention filters benefits the activity recognition, and we visualize the learned latent sub-events.

Connected through hyperedge HE-1 (induced by source paper "a correlation based feature representation for first person activity recognition"):
  with members:
    - "convolutional long short term memory networks for recognizing first person interactions"
    - "temporal segment networks towards good practices for deep action recognition"
    - "spatiotemporal residual networks for video action recognition"
    - "deep residual learning for image recognition"
    - "draw a recurrent neural network for image generation"
    - "the evolution of first person vision methods a survey"
    - "action recognition with trajectory pooled deep convolutional descriptors"
    - "analyzing the performance of multilayer neural networks for object recognition"

Connected through hyperedge HE-2 (induced by source paper "the pros and cons rank aware temporal attention for skill determination in long videos"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "learning to score the figure skating sports videos"
    - "w talc weakly supervised temporal activity localization and classification"
    - "deep learning with convolutional neural network for objective skill evaluation in robot assisted surgery"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "diversity regularized spatiotemporal attention for video based person re identification"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "quo vadis action recognition a new model and the kinetics dataset"

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)