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: "robust vertex enumeration for convex hulls in high dimensions"
  Abstract: Computation of the vertices of the convex hull of a set $S$ of $n$ points in $\mathbb{R} ^m$ is a fundamental problem in computational geometry, optimization, machine learning and more. We present "All Vertex Triangle Algorithm" (AVTA), a robust and efficient algorithm for computing the subset $\overline S$ of all $K$ vertices of $conv(S)$, the convex hull of $S$. If $\Gamma_*$ is the minimum of the distances from each vertex to the convex hull of the remaining vertices, given any $\gamma \leq \gamma_* = \Gamma_*/R$, $R$ the diameter of $S$, $AVTA$ computes $\overline S$ in $O(nK(m+ \gamma^{-2}))$ operations. If $\gamma_*$ is unknown but $K$ is known, AVTA computes $\overline S$ in $O(nK(m+ \gamma_*^{-2})) \log(\gamma_*^{-1})$ operations. More generally, given $t \in (0,1)$, AVTA computes a subset $\overline S^t$ of $\overline S$ in $O(n |\overline S^t|(m+ t^{-2}))$ operations, where the distance between any $p \in conv(S)$ to $conv(\overline S^t)$ is at most $t R$. Next we consider AVTA where input is $S_\varepsilon$, an $\varepsilon$ perturbation of $S$. Assuming a bound on $\varepsilon$ in terms of the minimum of the distances of vertices of $conv(S)$ to the convex hull of the remaining point of $S$, we derive analogous complexity bounds for computing $\overline S_\varepsilon$. We also analyze AVTA under random projections of $S$ or $S_\varepsilon$. Finally, via AVTA we design new practical algorithms for two popular machine learning problems: topic modeling and non-negative matrix factorization. For topic models AVTA leads to significantly better reconstruction of the topic-word matrix than state of the art approaches~\cite{arora2013practical, bansal2014provable}. For non-negative matrix AVTA is competitive with existing methods~\cite{arora2012computing}. Empirically AVTA is robust and can handle larger amounts of noise than existing methods.

Connected through hyperedge HE-1 (induced by source paper "spherical triangle algorithm a fast oracle for convex hull membership queries"):
  with members:
    - "sparse approximation via generating point sets"
    - "an algorithmic separating hyperplane theorem and its applications"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "a characterization theorem and an algorithm for a convex hull problem"

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.CG(Computational Geometry)