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: "matroidal degree bounded minimum spanning trees"
  Abstract: We consider the minimum spanning tree (MST) problem under the restriction that for every vertex v, the edges of the tree that are adjacent to v satisfy a given family of constraints. A famous example thereof is the classical degree-constrained MST problem, where for every vertex v, a simple upper bound on the degree is imposed. Iterative rounding/relaxation algorithms became the tool of choice for degree-bounded network design problems. A cornerstone for this development was the work of Singh and Lau, who showed for the degree-bounded MST problem how to find a spanning tree violating each degree bound by at most one unit and with cost at most the cost of an optimal solution that respects the degree bounds. #R##N#However, current iterative rounding approaches face several limits when dealing with more general degree constraints. In particular, when several constraints are imposed on the edges adjacent to a vertex v, as for example when a partition of the edges adjacent to v is given and only a fixed number of elements can be chosen out of each set of the partition, current approaches might violate each of the constraints by a constant, instead of violating all constraints together by at most a constant number of edges. Furthermore, it is also not clear how previous iterative rounding approaches can be used for degree constraints where some edges are in a super-constant number of constraints. #R##N#We extend iterative rounding/relaxation approaches both on a conceptual level as well as aspects involving their analysis to address these limitations. This leads to an efficient algorithm for the degree-constrained MST problem where for every vertex v, the edges adjacent to v have to be independent in a given matroid. The algorithm returns a spanning tree T of cost at most OPT, such that for every vertex v, it suffices to remove at most 8 edges from T to satisfy the matroidal degree constraint at v.

Connected through hyperedge HE-1 (induced by source paper "degree bounded generalized polymatroids and approximating the metric many visits tsp"):
  with members:
    - "time and space optimal algorithms for the many visits tsp"
    - "a constant factor approximation algorithm for the asymmetric traveling salesman problem"
    - "on generalizations of network design problems with degree bounds"

Connected through hyperedge HE-2 (induced by source paper "approximate multi matroid intersection via iterative refinement"):
  with members:
    - "generalized hypergraph matching via iterated packing and local ratio"
    - "improved approximation for 3 dimensional matching via bounded pathwidth local search"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"
    - "robust and maxmin optimization under matroid and knapsack uncertainty sets"
    - "constrained non monotone submodular maximization offline and secretary algorithms"
    - "on generalizations of network design problems with degree bounds"

Connected through hyperedge HE-3 (induced by source paper "approximating min cost chain constrained spanning trees a reduction from weighted to unweighted problems"):
  with members:
    - "approximation friendly discrepancy rounding"
    - "on generalizations of network design problems with degree bounds"

Connected through hyperedge HE-4 (induced by source paper "k trails recognition complexity and approximations"):
  with members:
    - "on generalizations of network design problems with degree bounds"

Connected through hyperedge HE-5 (induced by source paper "matroid and knapsack center problems"):
  with members:
    - "improved approximation algorithms for matroid and knapsack median problems and applications"
    - "lp rounding for k centers with non uniform hard capacities"
    - "clustering with diversity"

Connected through hyperedge HE-6 (induced by source paper "a new approximation technique for resource allocation problems"):
  with members:
    - "on generalizations of network design problems with degree bounds"
    - "santa claus schedules jobs on unrelated machines"
    - "approximating scheduling machines with capacity constraints"
    - "on allocating goods to maximize fairness"
    - "scheduling with outliers"
    - "adaptive uncertainty resolution in bayesian combinatorial optimization problems"

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.DS(Data Structures and Algorithms)