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: "non linear temporal subspace representations for activity recognition"
  Abstract: Representations that can compactly and effectively capture the temporal evolution of semantic content are important to computer vision and machine learning algorithms that operate on multi-variate time-series data. We investigate such representations motivated by the task of human action recognition. Here each data instance is encoded by a multivariate feature (such as via a deep CNN) where action dynamics are characterized by their variations in time. As these features are often non-linear, we propose a novel pooling method, kernelized rank pooling, that represents a given sequence compactly as the pre-image of the parameters of a hyperplane in a reproducing kernel Hilbert space, projections of data onto which captures their temporal order. We develop this idea further and show that such a pooling scheme can be cast as an order-constrained kernelized PCA objective. We then propose to use the parameters of a kernelized low-rank feature subspace as the representation of the sequences. We cast our formulation as an optimization problem on generalized Grassmann manifolds and then solve it efficiently using Riemannian optimization techniques. We present experiments on several action recognition datasets using diverse feature modalities and demonstrate 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 "three stream fusion network for first person interaction recognition"):
  with members:
    - "robust and discriminative labeling for multi label active learning based on maximum correntropy criterion"
    - "spatiotemporal pyramid network for video action recognition"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "boosted multiple kernel learning for first person activity recognition"
    - "attentional pooling for action recognition"
    - "convolutional long short term memory networks for recognizing first person interactions"
    - "action recognition with coarse to fine deep feature integration and asynchronous fusion"
    - "going deeper into first person activity 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"
    - "video representation learning using discriminative pooling"
    - "higher order pooling of cnn features via kernel linearization for action recognition"
    - "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"
    - "higher order pooling of cnn features via kernel linearization for action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-5 (induced by source paper "target specific action classification for automated assessment of human motor behavior from video"):
  with members:
    - "vision based assessment of parkinsonism and levodopa induced dyskinesia with pose estimation"
    - "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns"
    - "video action transformer network"
    - "unsupervised person re identification by deep learning tracklet association"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "background subtraction via fast robust matrix completion"
    - "posetrack a benchmark for human pose estimation and tracking"
    - "detect and track efficient pose estimation in videos"

Connected through hyperedge HE-6 (induced by source paper "learning discriminative video representations using adversarial perturbations"):
  with members:
    - "video representation learning using discriminative pooling"
    - "deep unsupervised saliency detection a multiple noisy labeling perspective"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "generalized rank pooling for activity recognition"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "safetynet detecting and rejecting adversarial examples robustly"
    - "lattice long short term memory for human action recognition"
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"

Connected through hyperedge HE-7 (induced by source paper "deep adaptive temporal pooling for activity recognition"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "efficient and deep person re identification using multi level similarity"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "attention is all you need"
    - "appearance and relation networks for video classification"
    - "untrimmednets for weakly supervised action recognition and detection"

Connected through hyperedge HE-8 (induced by source paper "deepgru deep gesture recognition utility"):
  with members:
    - "exploiting recurrent neural networks and leap motion controller for the recognition of sign language and semaphoric hand gestures"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "cnn based action recognition and supervised domain adaptation on 3d body skeletons via kernel feature maps"
    - "sts classification with dual stream cnn"
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "excitation backprop for rnns"
    - "motion feature augmented recurrent neural network for skeleton based dynamic hand gesture 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)