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: "deep models for relational databases"
  Abstract: Due to its extensive use in databases, the relational model is ubiquitous in representing big-data. We propose to apply deep learning to this type of relational data by introducing an Equivariant Relational Layer (ERL), a neural network layer derived from the entity-relationship model of the database. Our layer relies on identification of exchangeabilities in the relational data(base), and their expression as a permutation group. We prove that an ERL is an optimal parameter-sharing scheme under the given exchangeability constraints, and subsumes recently introduced deep models for sets, exchangeable tensors, and graphs. The proposed model has a linear complexity in the size of the relational data, and it can be used for both inductive and transductive reasoning in databases, including the prediction of missing records, and database embedding. This opens the door to the application of deep learning to one of the most abundant forms of data.

Connected through hyperedge HE-1 (induced by source paper "on learning sets of symmetric elements"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "incidence networks for geometric deep learning"
    - "on the limitations of representing functions on sets"
    - "permutation invariant feature restructuring for correlation aware image set based recognition"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "multiview aggregation for learning category specific shape reconstruction"
    - "on the universality of invariant networks"
    - "a simple proof of the universality of invariant equivariant graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "universal equivariant multilayer perceptrons"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "universal approximations of permutation invariant equivariant functions by deep neural networks"
    - "incidence networks for geometric deep learning"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "on the universality of invariant networks"
    - "on universal equivariant set networks"
    - "deep sets"
    - "invariant and equivariant graph networks"

Connected through hyperedge HE-3 (induced by source paper "incidence networks for geometric deep learning"):
  with members:
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "on the universality of invariant networks"
    - "on the equivalence between graph isomorphism testing and function approximation with gnns"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "relational inductive biases deep learning and graph networks"
    - "invariant and equivariant graph networks"
    - "intertwiners between induced representations with applications to the theory of equivariant neural networks"
    - "covariant compositional networks for learning graphs"

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)