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: "appearance and relation networks for video classification"
  Abstract: Spatiotemporal feature learning in videos is a fundamental problem in computer vision. This paper presents a new architecture, termed as Appearance-and-Relation Network (ARTNet), to learn video representation in an end-to-end manner. ARTNets are constructed by stacking multiple generic building blocks, called as SMART, whose goal is to simultaneously model appearance and relation from RGB input in a separate and explicit manner. Specifically, SMART blocks decouple the spatiotemporal learning module into an appearance branch for spatial modeling and a relation branch for temporal modeling. The appearance branch is implemented based on the linear combination of pixels or filter responses in each frame, while the relation branch is designed based on the multiplicative interactions between pixels or filter responses across multiple frames. We perform experiments on three action recognition benchmarks: Kinetics, UCF101, and HMDB51, demonstrating that SMART blocks obtain an evident improvement over 3D convolutions for spatiotemporal feature learning. Under the same training setting, ARTNets achieve superior performance on these three datasets to the existing state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "semantic image networks for human action recognition"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "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"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"

Connected through hyperedge HE-2 (induced by source paper "discriminative video representation learning using support vector classifiers"):
  with members:
    - "moments in time dataset one million videos for event understanding"
    - "a short note about kinetics 600"
    - "non linear temporal subspace representations for activity recognition"
    - "video representation learning using discriminative pooling"
    - "higher order pooling of cnn features via kernel linearization for action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "generalized rank pooling for activity recognition"
    - "interpretable 3d human action analysis with temporal convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "explainable video action reasoning via prior knowledge and state transitions"):
  with members:
    - "referring relationships"
    - "what have we learned from deep representations for action recognition"
    - "learning human object interactions by graph parsing neural networks"
    - "videos as space time region graphs"
    - "joint discovery of object states and manipulation actions"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "detecting visual relationships with deep relational networks"

Connected through hyperedge HE-4 (induced by source paper "human activity recognition for edge devices"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "temporal segment networks towards good practices for deep action recognition"
    - "long term temporal convolutions for action recognition"
    - "beyond short snippets deep networks for video classification"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "tsm temporal shift module for efficient video understanding"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "motion feature network fixed motion filter for action recognition"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "shift based primitives for efficient convolutional neural networks"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "haq hardware aware automated quantization with mixed precision"

Connected through hyperedge HE-6 (induced by source paper "higher order network for action recognition"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "learning spatio temporal representation with local and global diffusion"
    - "eco efficient convolutional network for online video understanding"
    - "video action transformer network"
    - "on the effectiveness of task granularity for transfer learning"
    - "videos as space time region graphs"
    - "slowfast networks for video recognition"

Connected through hyperedge HE-7 (induced by source paper "dynamic graph modules for modeling object object interactions in activity recognition"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "higher order network for action recognition"
    - "n ary relation extraction using graph state lstm"
    - "on the effectiveness of task granularity for transfer learning"
    - "relational inductive biases deep learning and graph networks"
    - "learning human object interactions by graph parsing neural networks"
    - "videos as space time region graphs"
    - "exploring graph structured passage representation for multi hop reading comprehension with graph neural networks"

Connected through hyperedge HE-8 (induced by source paper "multi fiber networks for video recognition"):
  with members:
    - "online detection of action start in untrimmed streaming videos"
    - "maskconnect connectivity learning by gradient descent"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "compressed video action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"

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)