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: "higher order pooling of cnn features via kernel linearization for action recognition"
  Abstract: Most successful deep learning algorithms for action recognition extend models designed for image-based tasks such as object recognition to video. Such extensions are typically trained for actions on single video frames or very short clips, and then their predictions from sliding-windows over the video sequence are pooled for recognizing the action at the sequence level. Usually this pooling step uses the first-order statistics of frame-level action predictions. In this paper, we explore the advantages of using higher-order correlations; specifically, we introduce Higher-order Kernel (HOK) descriptors generated from the late fusion of CNN classifier scores from all the frames in a sequence. To generate these descriptors, we use the idea of kernel linearization. Specifically, a similarity kernel matrix, which captures the temporal evolution of deep classifier scores, is first linearized into kernel feature maps. The HOK descriptors are then generated from the higher-order co-occurrences of these feature maps, and are then used as input to a video-level classifier. We provide experiments on two fine-grained action recognition datasets and show that our scheme leads to state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "hallucinating statistical moment and subspace descriptors from object and saliency detectors for action recognition"):
  with members:
    - "hallucinating optical flow features for video classification"
    - "few shot learning via saliency guided hallucination of samples"
    - "assemblenet searching for multi stream neural connectivity in video architectures"
    - "hallucinating idt descriptors and i3d optical flow features for action recognition with cnns"
    - "deeply supervised salient object detection with short connections"
    - "learning discriminative video representations using adversarial perturbations"
    - "evolving space time neural architectures for videos"
    - "a deeper look at power normalizations"

Connected through hyperedge HE-2 (induced by source paper "a comparative review of recent kinect based action recognition algorithms"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "two stream adaptive graph convolutional networks for skeleton based action recognition"
    - "cnn based action recognition and supervised domain adaptation on 3d body skeletons via kernel feature maps"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "spatio temporal graph convolution for skeleton based action recognition"

Connected through hyperedge HE-3 (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"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "generalized rank pooling for activity recognition"
    - "appearance and relation networks for video classification"
    - "interpretable 3d human action analysis with temporal convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "hallucinating idt descriptors and i3d optical flow features for action recognition with cnns"):
  with members:
    - "loss switching fusion with similarity search for video classification"
    - "a comparative review of recent kinect based action recognition algorithms"
    - "long term feature banks for detailed video understanding"
    - "learning discriminative video representations using adversarial perturbations"
    - "cnn based action recognition and supervised domain adaptation on 3d body skeletons via kernel feature maps"
    - "a deeper look at power normalizations"
    - "non linear temporal subspace representations for activity recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-5 (induced by source paper "low rank random tensor for bilinear pooling"):
  with members:
    - "ms tcn multi stage temporal convolutional network for action segmentation"
    - "local temporal bilinear pooling for fine grained action parsing"
    - "bilinear attention networks"
    - "hierarchical bilinear pooling for fine grained visual recognition"
    - "mutan multimodal tucker fusion for visual question answering"
    - "is second order information helpful for large scale visual recognition"
    - "attentional pooling for action recognition"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"

Connected through hyperedge HE-6 (induced by source paper "local temporal bilinear pooling for fine grained action parsing"):
  with members:
    - "learning motion in feature space locally consistent deformable convolution networks for fine grained action detection"
    - "compact generalized non local network"
    - "temporal human action segmentation via dynamic clustering"
    - "monet moments embedding network"
    - "hierarchical bilinear pooling for fine grained visual recognition"
    - "a deeper look at power normalizations"
    - "second order democratic aggregation"
    - "statistically motivated second order pooling"

Connected through hyperedge HE-7 (induced by source paper "non linear temporal subspace representations for activity recognition"):
  with members:
    - "video representation learning using discriminative pooling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the kinetics human action video dataset"
    - "rank pooling for action recognition"
    - "ordered pooling of optical flow sequences for action recognition"
    - "kernelized covariance for action recognition"
    - "convolutional pose machines"
    - "riemannian stochastic variance reduced gradient on grassmann manifold"

Connected through hyperedge HE-8 (induced by source paper "second order temporal pooling for action recognition"):
  with members:
    - "moments in time dataset one million videos for event understanding"
    - "non linear temporal subspace representations for activity recognition"
    - "video representation learning using discriminative pooling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "generalized rank pooling for activity recognition"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "the kinetics human action video dataset"
    - "ava a video dataset of spatio temporally localized atomic visual actions"

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)