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: "transferable feature representation for visible to infrared cross dataset human action recognition"
  Abstract: Recently, infrared human action recognition has attracted increasing attention for it has many advantages over visible light, that is, being robust to illumination change and shadows. However, the infrared action data is limited until now, which degrades the performance of infrared action recognition. Motivated by the idea of transfer learning, an infrared human action recognition framework using auxiliary data from visible light is proposed to solve the problem of limited infrared action data. In the proposed framework, we first construct a novel Cross-Dataset Feature Alignment and Generalization (CDFAG) framework to map the infrared data and visible light data into a common feature space, where Kernel Manifold Alignment (KEMA) and a dual alignedto-generalized encoders (AGE) model are employed to represent the feature. Then, a support vector machine (SVM) is trained, using both the infrared data and visible light data, and can classify the features derived from infrared data. The proposed method is evaluated on InfAR, which is a publicly available infrared human action dataset. To build up auxiliary data, we set up a novel visible light action dataset XD145. Experimental results show that the proposed method can achieve state-of-the-art performance compared with several transfer learning and domain adaptation methods.

Connected through hyperedge HE-1 (induced by source paper "deep image to video adaptation and fusion networks for action recognition"):
  with members:
    - "hierarchically learned view invariant representations for cross view action recognition"
    - "combined static and motion features for deep networks based activity recognition in videos"
    - "global temporal representation based cnns for infrared action recognition"
    - "deep cross media knowledge transfer"
    - "exploiting images for video recognition with hierarchical generative adversarial networks"
    - "visual domain adaptation with manifold embedded distribution alignment"
    - "semantic autoencoder for zero shot learning"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-2 (induced by source paper "global temporal representation based cnns for infrared action recognition"):
  with members:
    - "leveraging structural context models and ranking score fusion for human interaction prediction"
    - "learning spatiotemporal features for infrared action recognition with 3d convolutional neural networks"
    - "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-3 (induced by source paper "hierarchically learned view invariant representations for cross view action recognition"):
  with members:
    - "global temporal representation based cnns for infrared action recognition"
    - "joint geometrical and statistical alignment for visual domain adaptation"
    - "learning a deep model for human action recognition from novel viewpoints"
    - "scatter component analysis a unified framework for domain adaptation and domain generalization"
    - "cross view action modeling learning and recognition"
    - "efficient learning of domain invariant image representations"
    - "marginalized denoising autoencoders for domain adaptation"
    - "a kernel method for the two sample problem"

Connected through hyperedge HE-4 (induced by source paper "pm gans discriminative representation learning for action recognition using partial modalities"):
  with members:
    - "learning spatiotemporal features for infrared action recognition with 3d convolutional neural networks"
    - "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)