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: "from manifold to manifold geometry aware dimensionality reduction for spd matrices"
  Abstract: Representing images and videos with Symmetric Positive Definite (SPD) matrices and considering the Riemannian geometry of the resulting space has proven beneficial for many recognition tasks. Unfortunately, computation on the Riemannian manifold of SPD matrices --especially of high-dimensional ones-- comes at a high cost that limits the applicability of existing techniques. In this paper we introduce an approach that lets us handle high-dimensional SPD matrices by constructing a lower-dimensional, more discriminative SPD manifold. To this end, we model the mapping from the high-dimensional SPD manifold to the low-dimensional one with an orthonormal projection. In particular, we search for a projection that yields a low-dimensional manifold with maximum discriminative power encoded via an affinity-weighted similarity measure based on metrics on the manifold. Learning can then be expressed as an optimization problem on a Grassmann manifold. Our evaluation on several classification tasks shows that our approach leads to a significant accuracy gain over state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "deep heterogeneous hashing for face video retrieval"):
  with members:
    - "the do s and don ts for cnn based face verification"
    - "is second order information helpful for large scale visual recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "hashnet deep learning to hash by continuation"
    - "fast training of triplet based deep binary embedding networks"
    - "deep supervised hashing with triplet labels"
    - "deep cross modal hashing"
    - "a riemannian network for spd matrix learning"

Connected through hyperedge HE-2 (induced by source paper "deep manifold embedding for hyperspectral image classification"):
  with members:
    - "diversity in machine learning"
    - "geometric understanding of deep learning"
    - "rethinking feature distribution for loss functions in image classification"
    - "mining on manifolds metric learning without labels"
    - "image reconstruction by domain transform manifold learning"
    - "deep class wise hashing semantics preserving hashing via class wise loss"
    - "going deeper with contextual cnn for hyperspectral image classification"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-3 (induced by source paper "jumping manifolds geometry aware dense non rigid structure from motion"):
  with members:
    - "a simple prior free method for non rigid structure from motion factorization revisited"
    - "non rigid structure from motion prior free factorization method revisited"
    - "dense depth estimation of a complex dynamic scene without explicit 3d motion estimation"
    - "scalable dense non rigid structure from motion a grassmannian perspective"
    - "monocular dense 3d reconstruction of a complex dynamic scene from two perspective frames"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "scalable dense monocular surface reconstruction"
    - "dense non rigid structure from motion made easy a spatial temporal smoothness based solution"

Connected through hyperedge HE-4 (induced by source paper "learning discriminative video representations using adversarial perturbations"):
  with members:
    - "non linear temporal subspace representations for activity recognition"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "unconstrained face identification using maximum likelihood of distances between deep off the shelf features"):
  with members:
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "organizing multimedia data in video surveillance systems based on face verification with convolutional neural networks"
    - "cortexnet a generic network family for robust visual temporal representations"
    - "maximum likelihood approximate nearest neighbor method in real time image recognition"
    - "unsupervised domain adaptation for face recognition in unlabeled videos"
    - "neural aggregation network for video face recognition"
    - "template adaptation for face verification and identification"
    - "viplfacenet an open source deep face recognition sdk"

Connected through hyperedge HE-6 (induced by source paper "statistically motivated second order pooling"):
  with members:
    - "a deeper look at power normalizations"
    - "second order democratic aggregation"
    - "is second order information helpful for large scale visual recognition"
    - "improved bilinear pooling with cnns"
    - "low rank bilinear pooling for fine grained classification"
    - "domain adaptation by mixture of alignments of second or higher order scatter tensors"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "densely connected convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "action attending graphic neural network"):
  with members:
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "an end to end spatio temporal attention model for human action recognition from skeleton data"
    - "deep learning on lie groups for skeleton based action recognition"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "a riemannian network for spd matrix learning"
    - "unsupervised learning of video representations using lstms"

Connected through hyperedge HE-8 (induced by source paper "automatic analysis of facial expressions based on deep covariance trajectories"):
  with members:
    - "deep covariance descriptors for facial expression recognition"
    - "covariance pooling for facial expression recognition"
    - "a novel geometric framework on gram matrix trajectories for human behavior understanding"
    - "island loss for learning discriminative features in facial expression recognition"
    - "automatic recognition of facial displays of unfelt emotions"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "going deeper in facial expression recognition using deep neural networks"
    - "peak piloted deep network for facial expression 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)