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: "new approximation algorithms for minimum enclosing convex shapes"
  Abstract: Given $n$ points in a $d$ dimensional Euclidean space, the Minimum Enclosing Ball (MEB) problem is to find the ball with the smallest radius which contains all $n$ points. We give a $O(nd\Qcal/\sqrt{\epsilon})$ approximation algorithm for producing an enclosing ball whose radius is at most $\epsilon$ away from the optimum (where $\Qcal$ is an upper bound on the norm of the points). This improves existing results using \emph{coresets}, which yield a $O(nd/\epsilon)$ greedy algorithm. Finding the Minimum Enclosing Convex Polytope (MECP) is a related problem wherein a convex polytope of a fixed shape is given and the aim is to find the smallest magnification of the polytope which encloses the given points. For this problem we present a $O(mnd\Qcal/\epsilon)$ approximation algorithm, where $m$ is the number of faces of the polytope. Our algorithms borrow heavily from convex duality and recently developed techniques in non-smooth optimization, and are in contrast with existing methods which rely on geometric arguments. In particular, we specialize the excessive gap framework of \citet{Nesterov05a} to obtain our results.

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 "minimum enclosing ball revisited stability and sub linear time algorithms"):
  with members:
    - "beyond worst case analysis"
    - "varepsilon coresets for clustering with outliers in doubling metrics"
    - "coresets and sketches"
    - "locating a small cluster privately"
    - "k center clustering under perturbation resilience"
    - "optimization algorithms for faster computational geometry"
    - "approximation and streaming algorithms for projective clustering via random projections"
    - "clustering under perturbation resilience"

Connected through hyperedge HE-3 (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-4 (induced by source paper "svm via saddle point optimization new bounds and distributed algorithms"):
  with members:
    - "optimization algorithms for faster computational geometry"
    - "sublinear optimization for machine learning"

Connected through hyperedge HE-5 (induced by source paper "linear coupling an ultimate unification of gradient and mirror descent"):
  with members:
    - "using optimization to break the epsilon barrier a faster and simpler width independent algorithm for solving positive linear programs in parallel"
    - "an almost linear time algorithm for approximate max flow in undirected graphs and its multicommodity generalizations"
    - "efficient accelerated coordinate descent methods and faster algorithms for solving linear systems"
    - "navigating central path with electrical flows from flows to matchings and back"
    - "nearly maximum flows in nearly linear time"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "a unified view of regularized dual averaging and mirror descent with implicit updates"
    - "adaptive bound optimization for online convex optimization"

Connected through hyperedge HE-6 (induced by source paper "using optimization to find maximum inscribed balls and minimum enclosing balls"):
  with members:
    - "using optimization to obtain a width independent parallel simpler and faster positive sdp solver"
    - "using optimization to break the epsilon barrier a faster and simpler width independent algorithm for solving positive linear programs in parallel"
    - "sublinear optimization for machine learning"

Connected through hyperedge HE-7 (induced by source paper "no dimension independent core sets for containment under homothetics"):
  with members:
    - "minimum enclosing polytope in high dimensions"

Connected through hyperedge HE-8 (induced by source paper "sublinear optimization for machine learning"):
  with members:
    - "online learning of noisy data with kernels"

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)