human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "tight bounds for approximate carath eodory and beyond"):
  Abstract: We give a deterministic nearly-linear time algorithm for approximating any point inside a convex polytope with a sparse convex combination of the polytope's vertices. Our result provides a constructive proof for the Approximate Carath\'{e}odory Problem, which states that any point inside a polytope contained in the $\ell_p$ ball of radius $D$ can be approximated to within $\epsilon$ in $\ell_p$ norm by a convex combination of only $O\left(D^2 p/\epsilon^2\right)$ vertices of the polytope for $p \geq 2$. We also show that this bound is tight, using an argument based on anti-concentration for the binomial distribution. #R##N#Along the way of establishing the upper bound, we develop a technique for minimizing norms over convex sets with complicated geometry; this is achieved by running Mirror Descent on a dual convex function obtained via Sion's Theorem. #R##N#As simple extensions of our method, we then provide new algorithms for submodular function minimization and SVM training. For submodular function minimization we obtain a simplification and (provable) speed-up over Wolfe's algorithm, the method commonly found to be the fastest in practice. For SVM training, we obtain $O(1/\epsilon^2)$ convergence for arbitrary kernels; each iteration only requires matrix-vector operations involving the kernel matrix, so we overcome the obstacle of having to explicitly store the kernel or compute its Cholesky factorization.

Includes members:
  Member "sparse approximation via generating point sets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximate guarantees for dictionary learning")
  Member "provable submodular minimization using wolfe s algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "quadratic decomposable submodular function minimization theory and practice")
  Member "toward a unified theory of sparse dimensionality reduction in euclidean space":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust and sample optimal algorithms for psd low rank approximation")
  Member "convex analysis and optimization with submodular functions a tutorial":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a submodularity based agglomerative clustering algorithm for the privacy funnel")
  Member "sequential and parallel algorithms for mixed packing and covering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed assignment with limited communication for multi robot multi target tracking")
  Member "on the number of iterations for dantzig wolfe optimization and packing covering approximation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximate degree secret sharing and concentration phenomena")

Question: Please tell me which class the source paper 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.DS(Data Structures and Algorithms)