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: "approximate cech complexes in low and high dimensions"
  Abstract: \v{C}ech complexes reveal valuable topological information about point sets at a certain scale in arbitrary dimensions, but the sheer size of these complexes limits their practical impact. While recent work introduced approximation techniques for filtrations of (Vietoris-)Rips complexes, a coarser version of \v{C}ech complexes, we propose the approximation of \v{C}ech filtrations directly. #R##N#For fixed dimensional point set $S$, we present an approximation of the \v{C}ech filtration of $S$ by a sequence of complexes of size linear in the number of points. We generalize well-separated pair decompositions (WSPD) to well-separated simplicial decomposition (WSSD) in which every simplex defined on $S$ is covered by some element of WSSD. We give an efficient algorithm to compute a linear-sized WSSD in fixed dimensional spaces. Using a WSSD, we then present a linear-sized approximation of the filtration of \v{C}ech complex of $S$. #R##N#We also present a generalization of the known fact that the Rips complex approximates the \v{C}ech complex by a factor of $\sqrt{2}$. We define a class of complexes that interpolate between \v{C}ech and Rips complexes and that, given any parameter $\eps > 0$, approximate the \v{C}ech complex by a factor $(1+\eps)$. Our complex can be represented by roughly $O(n^{\lceil 1/2\eps\rceil})$ simplices without any hidden dependence on the ambient dimension of the point set. Our results are based on an interesting link between \v{C}ech complex and coresets for minimum enclosing ball of high-dimensional point sets. As a consequence of our analysis, we show improved bounds on coresets that approximate the radius of the minimum enclosing ball.

Connected through hyperedge HE-1 (induced by source paper "persistent homology of geospatial data a case study with voting"):
  with members:
    - "gerrymandering and compactness implementation flexibility and abuse"
    - "the topological shape of brexit"

Connected through hyperedge HE-2 (induced by source paper "the offset filtration of convex objects"):
  with members:
    - "the compressed annotation matrix an efficient data structure for computing persistent cohomology"
    - "graph induced complex on point data"
    - "linear size approximations to the vietoris rips filtration"
    - "towards persistence based reconstruction in euclidean spaces"

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)