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: "minimizing sum of non convex but piecewise log lipschitz functions using coresets"
  Abstract: We suggest a new optimization technique for minimizing the sum $\sum_{i=1}^n f_i(x)$ of $n$ non-convex real functions that satisfy a property that we call piecewise log-Lipschitz. This is by forging links between techniques in computational geometry, combinatorics and convex optimization. #R##N#Example applications include the first constant-factor approximation algorithms whose running-time is polynomial in $n$ for the following fundamental problems: #R##N#(i) Constrained $\ell_z$ Linear Regression: Given $z>0$, $n$ vectors $p_1,\cdots,p_n$ on the plane, and a vector $b\in\mathbb{R}^n$, compute a unit vector $x$ and a permutation $\pi:[n]\to[n]$ that minimizes $\sum_{i=1}^n |p_ix-b_{\pi(i)}|^z$. #R##N#(ii) Points-to-Lines alignment: Given $n$ lines $\ell_1,\cdots,\ell_n$ on the plane, compute the matching $\pi:[n]\to[n]$ and alignment (rotation matrix $R$ and a translation vector $t$) that minimize the sum of Euclidean distances \[ \sum_{i=1}^n \mathrm{dist}(Rp_i-t,\ell_{\pi(i)})^z \] between each point to its corresponding line. #R##N#These problems are open even if $z=1$ and the matching $\pi$ is given. In this case, the running time of our algorithms reduces to $O(n)$ using core-sets that support: streaming, dynamic, and distributed parallel computations (e.g. on the cloud) in poly-logarithmic update time. Generalizations for handling e.g. outliers or pseudo-distances such as $M$-estimators for these problems are also provided. #R##N#Experimental results show that our provable algorithms improve existing heuristics also in practice. A demonstration in the context of Augmented Reality show how such algorithms may be used in real-time systems.

Connected through hyperedge HE-1 (induced by source paper "coresets for gaussian mixture models of any shape"):
  with members:
    - "map inference via block coordinate frank wolfe algorithm"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "learning mixture of gaussians with streaming data"
    - "on the k means median cost function"
    - "secure search on the cloud via coresets and sketches"
    - "pymanopt a python toolbox for optimization on manifolds using automatic differentiation"
    - "new frameworks for offline and streaming coreset constructions"
    - "k means for streaming and distributed big sparse data"

Connected through hyperedge HE-2 (induced by source paper "provable approximations for constrained ell_p regression"):
  with members:
    - "l1 norm error function robustness and outlier regularization"
    - "new frameworks for offline and streaming coreset constructions"
    - "on the sensitivity of shape fitting problems"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-3 (induced by source paper "k means clustering of lines for big data"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "a unified framework for approximating and clustering data"
    - "relative p epsilon approximations in geometry"

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)