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: "spatiotemporal pyramid network for video action recognition"
  Abstract: Two-stream convolutional networks have shown strong performance in video action recognition tasks. The key idea is to learn spatiotemporal features by fusing convolutional networks spatially and temporally. However, it remains unclear how to model the correlations between the spatial and temporal structures at multiple abstraction levels. First, the spatial stream tends to fail if two videos share similar backgrounds. Second, the temporal stream may be fooled if two actions resemble in short snippets, though appear to be distinct in the long term. We propose a novel spatiotemporal pyramid network to fuse the spatial and temporal features in a pyramid structure such that they can reinforce each other. From the architecture perspective, our network constitutes hierarchical fusion strategies which can be trained as a whole using a unified spatiotemporal loss. A series of ablation experiments support the importance of each fusion strategy. From the technical perspective, we introduce the spatiotemporal compact bilinear operator into video analysis tasks. This operator enables efficient training of bilinear fusion operations which can capture full interactions between the spatial and temporal features. Our final network achieves state-of-the-art results on standard video datasets.

Connected through hyperedge HE-1 (induced by source paper "semantic image networks for human action recognition"):
  with members:
    - "multi fiber networks for video recognition"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "spatio temporal channel correlation networks for action classification"
    - "a 2 nets double attention networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "appearance and relation networks for video classification"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"

Connected through hyperedge HE-2 (induced by source paper "modality compensation network cross modal adaptation for action recognition"):
  with members:
    - "temporal bilinear networks for video action recognition"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "chained multi stream networks exploiting pose motion and appearance for action classification and detection"
    - "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"
    - "pku mmd a large scale benchmark for continuous multi modal human action understanding"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"

Connected through hyperedge HE-3 (induced by source paper "three stream fusion network for first person interaction recognition"):
  with members:
    - "robust and discriminative labeling for multi label active learning based on maximum correntropy criterion"
    - "non linear temporal subspace representations for activity recognition"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "boosted multiple kernel learning for first person activity recognition"
    - "attentional pooling for action recognition"
    - "convolutional long short term memory networks for recognizing first person interactions"
    - "action recognition with coarse to fine deep feature integration and asynchronous fusion"
    - "going deeper into first person activity recognition"

Connected through hyperedge HE-4 (induced by source paper "adversarial pyramid network for video domain generalization"):
  with members:
    - "temporal attentive alignment for video domain adaptation"
    - "feature critic networks for heterogeneous domain generalization"
    - "stm spatiotemporal and motion encoding for action recognition"
    - "domain generalization by solving jigsaw puzzles"
    - "domain generalization via model agnostic learning of semantic features"
    - "generalizing across domains via cross gradient training"
    - "generalizing to unseen domains via adversarial data augmentation"
    - "tsm temporal shift module for efficient video understanding"

Connected through hyperedge HE-5 (induced by source paper "stm spatiotemporal and motion encoding for action recognition"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "end to end learning of motion representation for video understanding"
    - "stnet local and global spatial temporal modeling for action recognition"
    - "motion feature network fixed motion filter for action recognition"
    - "videos as space time region graphs"
    - "spatio temporal channel correlation networks for action classification"
    - "slowfast networks for video recognition"
    - "tsm temporal shift module for efficient video understanding"

Connected through hyperedge HE-6 (induced by source paper "mobile video action recognition"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "videocapsulenet a simplified network for action detection"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "face focused cross stream network for deception detection in videos"
    - "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "compressed video action recognition"

Connected through hyperedge HE-7 (induced by source paper "if ttn information fused temporal transformation network for video action recognition"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "compressed video action recognition"
    - "lattice long short term memory for human action recognition"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "convnet architecture search for spatiotemporal feature learning"
    - "optical flow guided feature a fast and robust motion representation for video action recognition"
    - "temporal relational reasoning in videos"
    - "temporal segment networks towards good practices for deep action recognition"

Connected through hyperedge HE-8 (induced by source paper "ontology based global and collective motion patterns for event classification in basketball videos"):
  with members:
    - "switching convolutional neural network for crowd counting"
    - "decorrelation of neutral vector variables theory and applications"
    - "spatiotemporal modeling for crowd counting in videos"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "convolutional two stream network fusion for video action recognition"
    - "beyond short snippets deep networks for video classification"
    - "a hierarchical deep temporal model for group activity recognition"
    - "detecting events and key actors in multi person 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)