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: "slow feature analysis for human action recognition"
  Abstract: Slow Feature Analysis (SFA) extracts slowly varying features from a quickly varying input signal. It has been successfully applied to modeling the visual receptive fields of the cortical neurons. Sufficient experimental results in neuroscience suggest that the temporal slowness principle is a general learning principle in visual perception. In this paper, we introduce the SFA framework to the problem of human action recognition by incorporating the discriminative information with SFA learning and considering the spatial relationship of body parts. In particular, we consider four kinds of SFA learning strategies, including the original unsupervised SFA (U-SFA), the supervised SFA (S-SFA), the discriminative SFA (D-SFA), and the spatial discriminative SFA (SD-SFA), to extract slow feature functions from a large amount of training cuboids which are obtained by random sampling in motion boundaries. Afterward, to represent action sequences, the squared first order temporal derivatives are accumulated over all transformed cuboids into one feature vector, which is termed the Accumulated Squared Derivative (ASD) feature. The ASD feature encodes the statistical distribution of slow features in an action sequence. Finally, a linear support vector machine (SVM) is trained to classify actions represented by ASD features. We conduct extensive experiments, including two sets of control experiments, two sets of large scale experiments on the KTH and Weizmann databases, and two sets of experiments on the CASIA and UT-interaction databases, to demonstrate the effectiveness of SFA for human action recognition.

Connected through hyperedge HE-1 (induced by source paper "unsupervised deep slow feature analysis for change detection in multi temporal remote sensing images"):
  with members:
    - "faster r cnn towards real time object detection with region proposal networks"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "discrimnet semi supervised action recognition from videos using generative adversarial networks"):
  with members:
    - "unsupervised representation learning by sorting sequences"
    - "nips 2016 tutorial generative adversarial networks"
    - "deep networks with stochastic depth"
    - "improved techniques for training gans"
    - "adversarial feature learning"
    - "self supervised video representation learning with odd one out networks"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "generating videos with scene dynamics"

Connected through hyperedge HE-3 (induced by source paper "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"):
  with members:
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "autoencoder with recurrent neural networks for video forgery detection"
    - "robust deep and inductive anomaly detection"
    - "abnormal event detection in videos using spatiotemporal autoencoder"
    - "training adversarial discriminators for cross channel abnormal event detection in crowds"
    - "anomaly detection in video using predictive convolutional long short term memory networks"
    - "image to image translation with conditional adversarial networks"
    - "learning temporal regularity in video sequences"

Connected through hyperedge HE-4 (induced by source paper "a cascaded convolutional neural network for single image dehazing"):
  with members:
    - "co saliency detection for rgbd images based on multi constraint feature matching and cross label propagation"

Connected through hyperedge HE-5 (induced by source paper "impression network for video object detection"):
  with members:
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "flow guided feature aggregation for video object detection"
    - "quality aware network for set to set recognition"
    - "deformable convolutional networks"
    - "semantic video cnns through representation warping"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "clockwork convnets for video semantic segmentation"
    - "deep feature flow for video recognition"

Connected through hyperedge HE-6 (induced by source paper "manifold regularized slow feature analysis for dynamic texture recognition"):
  with members:
    - "dynamic texture and scene classification by transferring deep image features"
    - "sa cnn dynamic scene classification using convolutional neural networks"
    - "beyond gauss image set matching on the riemannian manifold of pdfs"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "shuffle and learn unsupervised learning using temporal order verification"):
  with members:
    - "generative image modeling using style and structure adversarial networks"
    - "learning image representations equivariant to ego motion"
    - "unsupervised visual representation learning by context prediction"
    - "watch and learn semi supervised learning of object detectors from videos"
    - "unsupervised learning of video representations using lstms"
    - "unsupervised learning of visual representations using videos"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning image representations tied to ego motion"

Connected through hyperedge HE-8 (induced by source paper "deep feature flow for video recognition"):
  with members:
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "stfcn spatio temporal fcn for semantic video segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "exploiting semantic information and deep matching for optical flow"
    - "optical flow estimation using a spatial pyramid network"
    - "optical flow with semantic segmentation and localized layers"
    - "joint optical flow and temporally consistent semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

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)