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 the irreducible representations of commutative lie groups"
  Abstract: We present a new probabilistic model of compact commutative Lie groups that produces invariant-equivariant and disentangled representations of data. To define the notion of disentangling, we borrow a fundamental principle from physics that is used to derive the elementary particles of a system from its symmetries. Our model employs a newfound Bayesian conjugacy relation that enables fully tractable probabilistic inference over compact commutative Lie groups -- a class that includes the groups that describe the rotation and cyclic translation of images. We train the model on pairs of transformed image patches, and show that the learned invariant representation is highly effective for classification.

Connected through hyperedge HE-1 (induced by source paper "learning group structure and disentangled representations of dynamical environments"):
  with members:
    - "hamiltonian generative networks"
    - "symmetry based disentangled representation learning requires interaction with environments"
    - "hamiltonian neural networks"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "decoupling feature extraction from policy learning assessing benefits of state representation learning in goal based robotics"
    - "a style based generator architecture for generative adversarial networks"
    - "neural ordinary differential equations"
    - "towards a definition of disentangled representations"

Connected through hyperedge HE-2 (induced by source paper "are disentangled representations helpful for abstract visual reasoning"):
  with members:
    - "learning to make analogies by contrasting abstract relational structure"
    - "learning disentangled representations with reference based variational autoencoders"
    - "disentangling factors of variation using few labels"
    - "on the fairness of disentangled representations"
    - "multi object representation learning with iterative variational inference"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "competitive training of mixtures of independent deep generative models"
    - "learning deep disentangled embeddings with the f statistic loss"

Connected through hyperedge HE-3 (induced by source paper "on the fairness of disentangled representations"):
  with members:
    - "are disentangled representations helpful for abstract visual reasoning"
    - "learning disentangled representations with reference based variational autoencoders"
    - "on variational bounds of mutual information"
    - "competitive training of mixtures of independent deep generative models"
    - "learning deep disentangled embeddings with the f statistic loss"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "visual reinforcement learning with imagined goals"
    - "isolating sources of disentanglement in variational autoencoders"

Connected through hyperedge HE-4 (induced by source paper "disentangling factors of variation using few labels"):
  with members:
    - "are disentangled representations helpful for abstract visual reasoning"
    - "learning disentangled representations with reference based variational autoencoders"
    - "on the fairness of disentangled representations"
    - "competitive training of mixtures of independent deep generative models"
    - "learning deep disentangled embeddings with the f statistic loss"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "visual reinforcement learning with imagined goals"
    - "isolating sources of disentanglement in variational autoencoders"

Connected through hyperedge HE-5 (induced by source paper "theory and evaluation metrics for learning disentangled representations"):
  with members:
    - "learning deep disentangled embeddings with the f statistic loss"
    - "isolating sources of disentanglement in variational autoencoders"
    - "disentangling factors of variation with cycle consistent variational auto encoders"
    - "recent advances in autoencoder based representation learning"
    - "towards a definition of disentangled representations"
    - "variational autoencoders pursue pca directions by accident"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "disentangling factors of variation in deep representations using adversarial training"

Connected through hyperedge HE-6 (induced by source paper "challenging common assumptions in the unsupervised learning of disentangled representations"):
  with members:
    - "competitive training of mixtures of independent deep generative models"
    - "learning deep disentangled embeddings with the f statistic loss"
    - "visual reinforcement learning with imagined goals"
    - "isolating sources of disentanglement in variational autoencoders"
    - "curiosity driven exploration of learned disentangled goal spaces"
    - "improving generalization for abstract reasoning tasks using disentangled feature representations"
    - "recent advances in autoencoder based representation learning"
    - "disentangled sequential autoencoder"

Connected through hyperedge HE-7 (induced by source paper "3d steerable cnns learning rotationally equivariant features in volumetric data"):
  with members:
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "roto translation covariant convolutional networks for medical image analysis"
    - "deep sets"
    - "3d g cnns for pulmonary nodule detection"
    - "cubenet equivariance to 3d rotation and translation"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"
    - "intertwiners between induced representations with applications to the theory of equivariant neural networks"
    - "why do deep convolutional networks generalize so poorly to small image transformations"

Connected through hyperedge HE-8 (induced by source paper "intertwiners between induced representations with applications to the theory of equivariant neural networks"):
  with members:
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "covariant compositional networks for learning graphs"
    - "learning steerable filters for rotation equivariant cnns"
    - "dynamic routing between capsules"
    - "harmonic networks deep translation and rotation equivariance"
    - "group equivariant convolutional networks"
    - "transformation properties of learned visual representations"

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)