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: "kernelized covariance for action recognition"
  Abstract: In this paper we aim at increasing the descriptive power of the covariance matrix, limited in capturing linear mutual dependencies between variables only. We present a rigorous and principled mathematical pipeline to recover the kernel trick for computing the covariance matrix, enhancing it to model more complex, non-linear relationships conveyed by the raw data. To this end, we propose Kernelized-COV, which generalizes the original covariance representation without compromising the efficiency of the computation. In the experiments, we validate the proposed framework against many previous approaches in the literature, scoring on par or superior with respect to the state of the art on benchmark datasets for 3D action recognition.

Connected through hyperedge HE-1 (induced by source paper "learning unbiased representations via mutual information backpropagation"):
  with members:
    - "on variational bounds of mutual information"
    - "flexibly fair representation learning by disentanglement"
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "beyond local nash equilibria for adversarial networks"
    - "invariant representations without adversarial training"
    - "learning not to learn training deep neural networks with biased data"
    - "mitigating unwanted biases with adversarial learning"
    - "generalizing across domains via cross gradient training"

Connected through hyperedge HE-2 (induced by source paper "non linear temporal subspace representations for activity recognition"):
  with members:
    - "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"
    - "the kinetics human action video dataset"
    - "rank pooling for action recognition"
    - "ordered pooling of optical flow sequences for action recognition"
    - "convolutional pose machines"
    - "riemannian stochastic variance reduced gradient on grassmann manifold"

Connected through hyperedge HE-3 (induced by source paper "a novel geometric framework on gram matrix trajectories for human behavior understanding"):
  with members:
    - "emotion recognition by body movement representation on the manifold of symmetric positive definite matrices"
    - "elastic functional coding of riemannian trajectories"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "an end to end spatio temporal attention model for human action recognition from skeleton data"
    - "shape distributions of nonlinear dynamical systems for video based inference"
    - "differential recurrent neural networks for action recognition"

Connected through hyperedge HE-4 (induced by source paper "scalable and compact 3d action recognition with approximated rbf kernel machines"):
  with members:
    - "a compact kernel approximation for 3d action recognition"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "joint distance maps based action recognition with convolutional neural networks"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "action recognition based on joint trajectory maps using convolutional neural networks"
    - "deep learning on lie groups for skeleton based action recognition"
    - "tensor representations via kernel linearization for action recognition from 3d skeletons"

Connected through hyperedge HE-5 (induced by source paper "when kernel methods meet feature learning log covariance network for action recognition from skeletal data"):
  with members:
    - "joint distance maps based action recognition with convolutional neural networks"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "action recognition based on joint trajectory maps using convolutional neural networks"
    - "deep learning on lie groups for skeleton based action recognition"
    - "tensor representations via kernel linearization for action recognition from 3d skeletons"
    - "a riemannian network for spd matrix learning"
    - "bregman divergences for infinite dimensional covariance matrices"

Connected through hyperedge HE-6 (induced by source paper "correlation alignment by riemannian metric for domain adaptation"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "unsupervised cross domain image generation"
    - "revisiting batch normalization for practical domain adaptation"
    - "coupled generative adversarial networks"
    - "deep coral correlation alignment for deep domain adaptation"
    - "return of frustratingly easy domain adaptation"
    - "simultaneous deep transfer across domains and tasks"
    - "learning transferable features with deep adaptation networks"

Connected through hyperedge HE-7 (induced by source paper "a compact kernel approximation for 3d action recognition"):
  with members:
    - "tensor representations via kernel linearization for action recognition from 3d skeletons"
    - "bregman divergences for infinite dimensional covariance matrices"
    - "classifying and visualizing motion capture sequences using deep neural networks"
    - "random feature maps for dot product kernels"

Connected through hyperedge HE-8 (induced by source paper "space time representation of people based on 3d skeletal data a review"):
  with members:
    - "simultaneous feature and body part learning for real time robot awareness of human behaviors"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "linear time online action detection from 3d skeletal data using bags of gesturelets"
    - "combining local appearance and holistic view dual source deep neural networks for human pose estimation"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "a discussion on the validation tests employed to compare human action recognition methods using the msr action3d dataset"
    - "cross view action modeling learning and recognition"
    - "hopc histogram of oriented principal components of 3d pointclouds for 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)