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: "top down attention recurrent vlad encoding for action recognition in videos"
  Abstract: Most recent approaches for action recognition from video leverage deep architectures to encode the video clip into a fixed length representation vector that is then used for classification. For this to be successful, the network must be capable of suppressing irrelevant scene background and extract the representation from the most discriminative part of the video. Our contribution builds on the observation that spatio-temporal patterns characterizing actions in videos are highly correlated with objects and their location in the video. We propose Top-down Attention Action VLAD (TA-VLAD), a deep recurrent architecture with built-in spatial attention that performs temporally aggregated VLAD encoding for action recognition from videos. We adopt a top-down approach of attention, by using class specific activation maps obtained from a deep CNN pre-trained for image classification, to weight appearance features before encoding them into a fixed-length video descriptor using Gated Recurrent Units. Our method achieves state of the art recognition accuracy on HMDB51 and UCF101 benchmarks.

Connected through hyperedge HE-1 (induced by source paper "an analysis of deep neural networks with attention for action recognition from a neurophysiological perspective"):
  with members:
    - "attention is all we need nailing down object centric attention for egocentric activity recognition"
    - "lsta long short term attention for egocentric action recognition"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deep networks can resemble human feed forward vision in invariant object recognition"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "lsta long short term attention for egocentric action recognition"):
  with members:
    - "making third person techniques recognize first person actions in egocentric videos"
    - "scaling egocentric vision the epic kitchens dataset"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "adding attentiveness to the neurons in recurrent neural networks"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "attention is all we need nailing down object centric attention for egocentric activity recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "attend and interact higher order object interactions for video understanding"

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)