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: "minimum enclosing polytope in high dimensions"
  Abstract: We study the problem of covering a given set of $n$ points in a high, $d$-dimensional space by the minimum enclosing polytope of a given arbitrary shape. We present algorithms that work for a large family of shapes, provided either only translations and no rotations are allowed, or only rotation about a fixed point is allowed; that is, one is allowed to only scale and translate a given shape, or scale and rotate the shape around a fixed point. Our algorithms start with a polytope guessed to be of optimal size and iteratively moves it based on a greedy principle: simply move the current polytope directly towards any outside point till it touches the surface. For computing the minimum enclosing ball, this gives a simple greedy algorithm with running time $O(nd/\eps)$ producing a ball of radius $1+\eps$ times the optimal. This simple principle generalizes to arbitrary convex shape when only translations are allowed, requiring at most $O(1/\eps^2)$ iterations. Our algorithm implies that {\em core-sets} of size $O(1/\eps^2)$ exist not only for minimum enclosing ball but also for any convex shape with a fixed orientation. A {\em Core-Set} is a small subset of $poly(1/\eps)$ points whose minimum enclosing polytope is almost as large as that of the original points. Although we are unable to combine our techniques for translations and rotations for general shapes, for the min-cylinder problem, we give an algorithm similar to the one in \cite{HV03}, but with an improved running time of $2^{O(\frac{1}{\eps^2}\log \frac{1}{\eps})} nd$.

Connected through hyperedge HE-1 (induced by source paper "minimum enclosing ball revisited stability sub linear time algorithms and extension"):
  with members:
    - "beyond worst case analysis"
    - "solving k center clustering with outliers in mapreduce and streaming almost as accurately as sequentially"
    - "distributed partial clustering"
    - "coresets and sketches"
    - "locating a small cluster privately"
    - "the non uniform k center problem"
    - "k center clustering under perturbation resilience"
    - "optimization algorithms for faster computational geometry"

Connected through hyperedge HE-2 (induced by source paper "clustering in hilbert simplex geometry"):
  with members:
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "an elementary introduction to information geometry"
    - "representation tradeoffs for hyperbolic embeddings"
    - "hyperbolic entailment cones for learning hierarchical embeddings"
    - "on holder projective divergences"
    - "fast 1 epsilon approximation of the lowner extremal matrices of high dimensional symmetric matrices"
    - "large margin nearest neighbor classification using curved mahalanobis distances"
    - "further heuristics for k means the merge and split heuristic and the k l means"

Connected through hyperedge HE-3 (induced by source paper "stochastic k center and j flat center problems"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "nearest neighbor searching under uncertainty ii"
    - "range queries on uncertain data"
    - "convex hulls under uncertainty"
    - "range counting coresets for uncertain data"
    - "on the sensitivity of shape fitting problems"
    - "approximating the expected values for combinatorial optimization problems over stochastic points"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-4 (induced by source paper "dimension reduction techniques for ell_p 1 le p infty with applications"):
  with members:
    - "approximate nearest neighbor search in ell_p"
    - "fast precise and dynamic distance queries"
    - "a nonlinear approach to dimension reduction"
    - "fast construction of nets in low dimensional metrics and their applications"
    - "entropy based nearest neighbor search in high dimensions"

Connected through hyperedge HE-5 (induced by source paper "no dimension independent core sets for containment under homothetics"):
  with members:
    - "new approximation algorithms for minimum enclosing convex shapes"

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)