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 spatiotemporal features for infrared action recognition with 3d convolutional neural networks"
  Abstract: Infrared (IR) imaging has the potential to enable more robust action recognition systems compared to visible spectrum cameras due to lower sensitivity to lighting conditions and appearance variability. While the action recognition task on videos collected from visible spectrum imaging has received much attention, action recognition in IR videos is significantly less explored. Our objective is to exploit imaging data in this modality for the action recognition task. In this work, we propose a novel two-stream 3D convolutional neural network (CNN) architecture by introducing the discriminative code layer and the corresponding discriminative code loss function. The proposed network processes IR image and the IR-based optical flow field sequences. We pretrain the 3D CNN model on the visible spectrum Sports-1M action dataset and finetune it on the Infrared Action Recognition (InfAR) dataset. To our best knowledge, this is the first application of the 3D CNN to action recognition in the IR domain. We conduct an elaborate analysis of different fusion schemes (weighted average, single and double-layer neural nets) applied to different 3D CNN outputs. Experimental results demonstrate that our approach can achieve state-of-the-art average precision (AP) performances on the InfAR dataset: (1) the proposed two-stream 3D CNN achieves the best reported 77.5% AP, and (2) our 3D CNN model applied to the optical flow fields achieves the best reported single stream 75.42% AP.

Connected through hyperedge HE-1 (induced by source paper "global temporal representation based cnns for infrared action recognition"):
  with members:
    - "transferable feature representation for visible to infrared cross dataset human action recognition"
    - "leveraging structural context models and ranking score fusion for human interaction prediction"
    - "joint distance maps based action recognition with convolutional neural networks"
    - "towards better exploiting convolutional neural networks for remote sensing scene classification"
    - "convolutional two stream network fusion for video action recognition"
    - "long term temporal convolutions for action recognition"
    - "action recognition with trajectory pooled deep convolutional descriptors"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-2 (induced by source paper "an action recognition network for specific target based on rmc and rpn"):
  with members:
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "ssd single shot multibox detector"
    - "xception deep learning with depthwise separable convolutions"
    - "r fcn object detection via region based fully convolutional networks"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "pm gans discriminative representation learning for action recognition using partial modalities"):
  with members:
    - "transferable feature representation for visible to infrared cross dataset human action recognition"
    - "cm gans cross modal generative adversarial networks for common representation learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "adversarial discriminative domain adaptation"
    - "rank pooling for action recognition"
    - "learning a deep model for human action recognition from novel viewpoints"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "temporal segment networks towards good practices for deep action recognition"

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)