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: "local group invariant representations via orbit embeddings"
  Abstract: Invariance to nuisance transformations is one of the desirable properties of effective representations. We consider transformations that form a \emph{group} and propose an approach based on kernel methods to derive local group invariant representations. Locality is achieved by defining a suitable probability distribution over the group which in turn induces distributions in the input feature space. We learn a decision function over these distributions by appealing to the powerful framework of kernel methods and generate local invariant random feature maps via kernel approximations. We show uniform convergence bounds for kernel approximation and provide excess risk bounds for learning with these features. We evaluate our method on three real datasets, including Rotated MNIST and CIFAR-10, and observe that it outperforms competing kernel based approaches. The proposed method also outperforms deep CNN on Rotated-MNIST and performs comparably to the recently proposed group-equivariant CNN.

Connected through hyperedge HE-1 (induced by source paper "a kernel theory of modern data augmentation"):
  with members:
    - "autoaugment learning augmentation policies from data"
    - "max margin invariant features from transformed unlabeled data"
    - "understanding deep learning requires rethinking generalization"
    - "identity mappings in deep residual networks"
    - "regularization with stochastic transformations and perturbations for deep semi supervised learning"
    - "fractional max pooling"
    - "discriminative unsupervised feature learning with exemplar convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "generalization properties of hyper rkhs and its application to out of sample extensions"):
  with members:
    - "deep kernel learning"
    - "a la carte learning fast kernels"
    - "convolutional kernel networks"
    - "a divide and conquer solver for kernel support vector machines"

Connected through hyperedge HE-3 (induced by source paper "variational inference of disentangled latent concepts from unlabeled observations"):
  with members:
    - "unsupervised learning of disentangled representations from video"
    - "towards deeper understanding of variational autoencoding models"
    - "understanding visual concepts with continuation learning"
    - "a survey of inductive biases for factorial representation learning"
    - "adversarial feature learning"
    - "deep feature consistent variational autoencoder"
    - "weakly supervised disentangling with recurrent transformations for 3d view synthesis"
    - "building machines that learn and think like people"

Connected through hyperedge HE-4 (induced by source paper "on the effect of pooling on the geometry of representations"):
  with members:
    - "exploiting cyclic symmetry in convolutional neural networks"
    - "group equivariant convolutional networks"
    - "learning with group invariant features a kernel perspective"
    - "understanding deep features with computer generated imagery"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"
    - "on invariance and selectivity in representation learning"
    - "understanding image representations by measuring their equivariance and equivalence"
    - "transformation properties of learned visual representations"

Connected through hyperedge HE-5 (induced by source paper "semi supervised learning with gans manifold invariance with improved inference"):
  with members:
    - "learning loss functions for semi supervised learning via discriminative adversarial networks"
    - "improved training of wasserstein gans"
    - "temporal ensembling for semi supervised learning"
    - "improved techniques for training gans"
    - "adversarial feature learning"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "learning with group invariant features a kernel perspective"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-6 (induced by source paper "max margin invariant features from transformed unlabeled data"):
  with members:
    - "unsupervised learning of invariant representations in hierarchical architectures"

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.LG(Machine Learning)