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: "tan temporal aggregation network for dense multi label action recognition"
  Abstract: We present Temporal Aggregation Network (TAN) which decomposes 3D convolutions into spatial and temporal aggregation blocks. By stacking spatial and temporal convolutions repeatedly, TAN forms a deep hierarchical representation for capturing spatio-temporal information in videos. Since we do not apply 3D convolutions in each layer but only apply temporal aggregation blocks once after each spatial downsampling layer in the network, we significantly reduce the model complexity. The use of dilated convolutions at different resolutions of the network helps in aggregating multi-scale spatio-temporal information efficiently. Experiments show that our model is well suited for dense multi-label action recognition, which is a challenging sub-topic of action recognition that requires predicting multiple action labels in each frame. We outperform state-of-the-art methods by 5% and 3% on the Charades and Multi-THUMOS dataset respectively.

Connected through hyperedge HE-1 (induced by source paper "man moment alignment network for natural language moment retrieval via iterative graph adjustment"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "learning to compose topic aware mixture of experts for zero shot video captioning"
    - "graph r cnn for scene graph generation"
    - "s3d single shot multi span detector via fully 3d convolutional networks"
    - "multilevel language and vision integration for text to clip retrieval"
    - "dynamic temporal pyramid network a closer look at multi scale modeling for activity detection"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "actor and action video segmentation from a sentence"

Connected through hyperedge HE-2 (induced by source paper "dynamic temporal pyramid network a closer look at multi scale modeling for activity detection"):
  with members:
    - "s3d single shot multi span detector via fully 3d convolutional networks"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"
    - "single shot temporal action detection"
    - "temporal action detection with structured segment networks"

Connected through hyperedge HE-3 (induced by source paper "horizontal pyramid matching for person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "multi level factorisation net for person re identification"
    - "harmonious attention network for person re identification"
    - "sta spatial temporal attention for large scale video based person re identification"
    - "in defense of the triplet loss for person re identification"
    - "glad global local alignment descriptor for pedestrian retrieval"
    - "alignedreid surpassing human level performance in person re identification"
    - "temporal context network for activity localization in videos"

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)