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: "solving empirical risk minimization in the current matrix multiplication time"
  Abstract: Many convex problems in machine learning and computer science share the same form: \begin{align*} \min_{x} \sum_{i} f_i( A_i x + b_i), \end{align*} where $f_i$ are convex functions on $\mathbb{R}^{n_i}$ with constant $n_i$, $A_i \in \mathbb{R}^{n_i \times d}$, $b_i \in \mathbb{R}^{n_i}$ and $\sum_i n_i = n$. This problem generalizes linear programming and includes many problems in empirical risk minimization. In this paper, we give an algorithm that runs in time \begin{align*} O^* ( ( n^{\omega} + n^{2.5 - \alpha/2} + n^{2+ 1/6} ) \log (n / \delta) ) \end{align*} where $\omega$ is the exponent of matrix multiplication, $\alpha$ is the dual exponent of matrix multiplication, and $\delta$ is the relative accuracy. Note that the runtime has only a log dependence on the condition numbers or other data dependent parameters and these are captured in $\delta$. For the current bound $\omega \sim 2.38$ [Vassilevska Williams'12, Le Gall'14] and $\alpha \sim 0.31$ [Le Gall, Urrutia'18], our runtime $O^* ( n^{\omega} \log (n / \delta))$ matches the current best for solving a dense least squares regression problem, a special case of the problem we consider. Very recently, [Alman'18] proved that all the current known techniques can not give a better $\omega$ below $2.168$ which is larger than our $2+1/6$. Our result generalizes the very recent result of solving linear programs in the current matrix multiplication time [Cohen, Lee, Song'19] to a more broad class of problems. Our algorithm proposes two concepts which are different from [Cohen, Lee, Song'19] : #R##N#$\bullet$ We give a robust deterministic central path method, whereas the previous one is a stochastic central path which updates weights by a random sparse vector. #R##N#$\bullet$ We propose an efficient data-structure to maintain the central path of interior point methods even when the weights update vector is dense.

Connected through hyperedge HE-1 (induced by source paper "solving tall dense linear programs in nearly linear time"):
  with members:
    - "dynamic matrix inverse improved algorithms and matching conditional lower bounds"
    - "dynamic streaming spectral sparsification in nearly linear time and space"
    - "fully dynamic spectral vertex sparsifiers and applications"
    - "a deterministic linear program solver in current matrix multiplication time"
    - "solving linear programs in the current matrix multiplication time"
    - "relative error tensor low rank approximation"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "optimality of the johnson lindenstrauss lemma"

Connected through hyperedge HE-2 (induced by source paper "barriers for rectangular matrix multiplication"):
  with members:
    - "limits on all known and some unknown approaches to matrix multiplication"
    - "solving linear programs in the current matrix multiplication time"
    - "limits on the universal method for matrix multiplication"
    - "barriers for fast matrix multiplication from irreversibility"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "further limitations of the known approaches for matrix multiplication"
    - "fast matrix multiplication limitations of the laser method"
    - "powers of tensors and fast matrix multiplication"

Connected through hyperedge HE-3 (induced by source paper "optimal sketching for kronecker product regression and low rank approximation"):
  with members:
    - "almost optimal tensor sketch"
    - "oblivious sketching of high degree polynomial kernels"
    - "a deterministic linear program solver in current matrix multiplication time"
    - "tight bounds for ell_p oblivious subspace embeddings"
    - "solving linear programs in the current matrix multiplication time"
    - "towards a zero one law for entrywise low rank approximation"
    - "sublinear time low rank approximation of distance matrices"
    - "relative error tensor low rank approximation"

Connected through hyperedge HE-4 (induced by source paper "a near optimal algorithm for approximating the john ellipsoid"):
  with members:
    - "total least squares regression in input sparsity time"
    - "nearly optimal dynamic k means clustering for high dimensional data"
    - "sensitivity sampling over dynamic geometric data streams with applications to k clustering"
    - "subspace embedding and linear regression with orlicz norm"
    - "relative error tensor low rank approximation"
    - "near optimal sketching of low rank tensor regression"
    - "sketching for kronecker product regression and p splines"
    - "approximate gaussian elimination for laplacians fast sparse and simple"

Connected through hyperedge HE-5 (induced by source paper "total least squares regression in input sparsity time"):
  with members:
    - "tight bounds for the subspace sketch problem with applications"
    - "optimal sketching for kronecker product regression and low rank approximation"
    - "a near optimal algorithm for approximating the john ellipsoid"
    - "dimensionality reduction for tukey regression"
    - "solving linear programs in the current matrix multiplication time"
    - "subspace embedding and linear regression with orlicz norm"
    - "relative error tensor low rank approximation"
    - "near optimal sketching of low rank tensor regression"

Connected through hyperedge HE-6 (induced by source paper "a deterministic linear program solver in current matrix multiplication time"):
  with members:
    - "dynamic matrix inverse improved algorithms and matching conditional lower bounds"
    - "distributed edge connectivity in sublinear time"
    - "limits on all known and some unknown approaches to matrix multiplication"
    - "solving linear programs in the current matrix multiplication time"
    - "derandomization beyond connectivity undirected laplacian systems in nearly logarithmic space"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "powers of tensors and fast matrix multiplication"
    - "path finding i solving linear programs with o sqrt rank linear system solves"

Connected through hyperedge HE-7 (induced by source paper "solving linear programs in the current matrix multiplication time"):
  with members:
    - "limits on all known and some unknown approaches to matrix multiplication"
    - "incomplete nested dissection"
    - "solving directed laplacian systems in nearly linear time through sparse lu factorizations"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "matrix scaling and balancing via box constrained newton s method and interior point methods"
    - "computing maximum flow with augmenting electrical flows"
    - "geometric median in nearly linear time"
    - "approximate gaussian elimination for laplacians fast sparse and simple"

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.DS(Data Structures and Algorithms)