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: "on the expressive power of query languages for matrices"
  Abstract: We investigate the expressive power of $\mathsf{MATLANG}$, a formal language for matrix manipulation based on common matrix operations and linear algebra. The language can be extended with the operation $\mathsf{inv}$ of inverting a matrix. In $\mathsf{MATLANG}+\mathsf{inv}$ we can compute the transitive closure of directed graphs, whereas we show that this is not possible without inversion. Indeed we show that the basic language can be simulated in the relational algebra with arithmetic operations, grouping, and summation. We also consider an operation $\mathsf{eigen}$ for diagonalizing a matrix, which is defined so that different eigenvectors returned for a same eigenvalue are orthogonal. We show that $\mathsf{inv}$ can be expressed in $\mathsf{MATLANG}+\mathsf{eigen}$. We put forward the open question whether there are boolean queries about matrices, or generic queries about graphs, expressible in $\mathsf{MATLANG} + \mathsf{eigen}$ but not in $\mathsf{MATLANG}+\mathsf{inv}$. The evaluation problem for $\mathsf{MATLANG} + \mathsf{eigen}$ is shown to be complete for the complexity class $\exists \mathbf{R}$.

Connected through hyperedge HE-1 (induced by source paper "on matrices and k relations"):
  with members:
    - "on the expressive power of linear algebra on graphs"
    - "laradb a minimalist kernel for linear and relational algebra computation"
    - "polystore mathematics of relational algebra"
    - "juggling functions inside a database"
    - "faq questions asked frequently"

Connected through hyperedge HE-2 (induced by source paper "on the expressiveness of lara a unified language for linear and relational algebra"):
  with members:
    - "on the expressive power of linear algebra on graphs"
    - "research directions for principles of data management dagstuhl perspectives workshop 16151"
    - "laradb a minimalist kernel for linear and relational algebra computation"

Connected through hyperedge HE-3 (induced by source paper "on the expressive power of linear algebra on graphs"):
  with members:
    - "on the expressiveness of lara a unified language for linear and relational algebra"
    - "laradb a minimalist kernel for linear and relational algebra computation"
    - "on the combinatorial power of the weisfeiler lehman algorithm"
    - "towards linear algebra over normalized data"
    - "descriptive complexity of graph spectra"
    - "foundations of modern query languages for graph databases"
    - "rank logic is dead long live rank logic"
    - "dimension reduction via colour refinement"

Connected through hyperedge HE-4 (induced by source paper "in database learning with sparse tensors"):
  with members:
    - "ac dc in database learning thunderstruck"
    - "research directions for principles of data management dagstuhl perspectives workshop 16151"
    - "size bounds and query plans for relational joins"
    - "constraint solving via fractional edge covers"
    - "towards linear algebra over normalized data"
    - "tensorflow a system for large scale machine learning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "mllib machine learning in apache spark"

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.DB(Databases)