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: "tensor network complexity of multilinear maps"
  Abstract: We study \emph{tensor networks} as a model of arithmetic computation for evaluating multilinear maps. This model turns out to be strong enough to capture the current best algorithms for a variety of problems, e.g., $O(n^{\omega + \epsilon})$ time matrix multiplication, $O(n \log n)$ time discrete Fourier transform, $O(n^{(\omega+\epsilon)t + j})$ time $(3t+j)$-clique counting and $O^*(2^n)$ time for computing the permanent of a matrix. #R##N#While powerful, the model still has limitations, and we are able to show a number of unconditional lower bounds for various linear maps, including: #R##N#(a) an $\Omega(n^{\lceil 2v/3 \rceil})$ time lower bound for $v$-clique, matching the upper bound if $\omega = 2$. #R##N#(b) an $\Omega(2^{0.918n})$ time lower bound for the permanent of an $n \times n$ matrix. #R##N#(c) an $\Omega(n^4)$ time lower bound for the trilinear map $D_{ijk} = \sum_{l} A_{ijl}B_{ikl} C_{jkl}$ generalizing matrix multiplication, taking three $n \times n \times n$ tensors $A, B, C$ and producing an $n \times n \times n$ tensor $D$, ruling out tensor networks as an approach to obtaining non-trivial algorithms for hyperclique counting and the Max-$3$-CSP problem.

Connected through hyperedge HE-1 (induced by source paper "machine learning basic principles"):
  with members:
    - "the logistic network lasso"
    - "asymptotic behavior of ell_p based laplacian regularization in semi supervised learning"
    - "the power of depth for feedforward neural networks"
    - "discrete signal processing on graphs sampling theory"
    - "signal recovery on graphs variation minimization"

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.CC(Computational Complexity)