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: "learning with group invariant features a kernel perspective"
  Abstract: We analyze in this paper a random feature map based on a theory of invariance I-theory introduced recently. More specifically, a group invariant signal signature is obtained through cumulative distributions of group transformed random projections. Our analysis bridges invariant feature learning with kernel methods, as we show that this feature map defines an expected Haar integration kernel that is invariant to the specified group action. We show how this non-linear random feature map approximates this group invariant kernel uniformly on a set of $N$ points. Moreover, we show that it defines a function space that is dense in the equivalent Invariant Reproducing Kernel Hilbert Space. Finally, we quantify error rates of the convergence of the empirical risk minimization, as well as the reduction in the sample complexity of a learning algorithm using such an invariant representation for signal classification, in a classical supervised learning setting.

Connected through hyperedge HE-1 (induced by source paper "parametrizing filters of a cnn with a gan"):
  with members:
    - "group equivariant convolutional networks"
    - "understanding deep image representations by inverting them"
    - "understanding image representations by measuring their equivariance and equivalence"

Connected through hyperedge HE-2 (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"
    - "local group invariant representations via orbit embeddings"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "on the effect of pooling on the geometry of representations"):
  with members:
    - "exploiting cyclic symmetry in convolutional neural networks"
    - "local group invariant representations via orbit embeddings"
    - "group equivariant convolutional networks"
    - "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-4 (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"
    - "local group invariant representations via orbit embeddings"
    - "temporal ensembling for semi supervised learning"
    - "improved techniques for training gans"
    - "adversarial feature learning"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-5 (induced by source paper "local group invariant representations via orbit embeddings"):
  with members:
    - "recycling randomness with structure for sublinear time kernel expansions"
    - "learning from conditional distributions via dual kernel embeddings"
    - "structured adaptive and random spinners for fast machine learning computations"
    - "group equivariant convolutional networks"
    - "on invariance and selectivity in representation learning"
    - "scalable kernel methods via doubly stochastic gradients"
    - "how to scale up kernel methods to be as good as deep neural nets"
    - "fastfood approximate kernel expansions in loglinear time"

Connected through hyperedge HE-6 (induced by source paper "learning from conditional distributions via dual kernel embeddings"):
  with members:
    - "scalable kernel methods via doubly stochastic gradients"
    - "breaking the curse of dimensionality with convex neural networks"
    - "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)