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: "dynamic texture and scene classification by transferring deep image features"
  Abstract: Dynamic texture and scene classification are two fundamental problems in understanding natural video content. Extracting robust and effective features is a crucial step towards solving these problems. However the existing approaches suffer from the sensitivity to either varying illumination, or viewpoint changing, or even camera motion, and/or the lack of spatial information. Inspired by the success of deep structures in image classification, we attempt to leverage a deep structure to extract feature for dynamic texture and scene classification. To tackle with the challenges in training a deep structure, we propose to transfer some prior knowledge from image domain to video domain. To be specific, we propose to apply a well-trained Convolutional Neural Network (ConvNet) as a mid-level feature extractor to extract features from each frame, and then form a representation of a video by concatenating the first and the second order statistics over the mid-level features. We term this two-level feature extraction scheme as a Transferred ConvNet Feature (TCoF). Moreover we explore two different implementations of the TCoF scheme, i.e., the \textit{spatial} TCoF and the \textit{temporal} TCoF, in which the mean-removed frames and the difference between two adjacent frames are used as the inputs of the ConvNet, respectively. We evaluate systematically the proposed spatial TCoF and the temporal TCoF schemes on three benchmark data sets, including DynTex, YUPENN, and Maryland, and demonstrate that the proposed approach yields superior performance.

Connected through hyperedge HE-1 (induced by source paper "driver drowsiness detection using condition adaptive representation learning framework"):
  with members:
    - "sparse 3d convolutional neural networks for large scale shape retrieval"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "multi view face detection using deep convolutional neural networks"
    - "show attend and tell neural image caption generation with visual attention"
    - "learning transferrable knowledge for semantic segmentation with deep convolutional neural network"
    - "learning deep representations of appearance and motion for anomalous event detection"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "deep discriminative model for video classification"):
  with members:
    - "rank pooling for action recognition"
    - "long term temporal convolutions for action recognition"
    - "human action recognition using factorized spatio temporal convolutional networks"
    - "beyond short snippets deep networks for video classification"
    - "action recognition with trajectory pooled deep convolutional descriptors"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "beyond gaussian pyramid multi skip feature stacking for action recognition"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-3 (induced by source paper "dynamic texture recognition using time causal and time recursive spatio temporal receptive fields"):
  with members:
    - "dense scale selection over space time and space time"
    - "alignment distances on systems of bags"
    - "convolutional neural network on three orthogonal planes for dynamic texture classification"
    - "time causal and time recursive spatio temporal receptive fields"
    - "spatiotemporal gabor filters a new method for dynamic texture recognition"

Connected through hyperedge HE-4 (induced by source paper "alignment distances on systems of bags"):
  with members:
    - "dictionary learning and sparse coding on grassmann manifolds an extrinsic solution"

Connected through hyperedge HE-5 (induced by source paper "convolutional neural network on three orthogonal planes for dynamic texture classification"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "texture segmentation with fully convolutional networks"
    - "using filter banks in convolutional neural networks for texture classification"
    - "learning rotation invariant convolutional filters for texture classification"
    - "a theoretical analysis of deep neural networks for texture classification"
    - "understanding neural networks through deep visualization"
    - "deep filter banks for texture recognition description and segmentation"
    - "bilinear cnn models for fine grained visual recognition"

Connected through hyperedge HE-6 (induced by source paper "manifold regularized slow feature analysis for dynamic texture recognition"):
  with members:
    - "slow feature analysis for human action recognition"
    - "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 "deep learning for surface material classification using haptic and visual information"):
  with members:
    - "using filter banks in convolutional neural networks for texture classification"
    - "deep learning for tactile understanding from visual and haptic data"
    - "deep learning and music adversaries"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"

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)