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: "combinatorial approximation algorithms for maxcut using random walks"
  Abstract: We give the first combinatorial approximation algorithm for Maxcut that beats the trivial 0.5 factor by a constant. The main partitioning procedure is very intuitive, natural, and easily described. It essentially performs a number of random walks and aggregates the information to provide the partition. We can control the running time to get an approximation factor-running time tradeoff. We show that for any constant b > 1.5, there is an O(n^{b}) algorithm that outputs a (0.5+delta)-approximation for Maxcut, where delta = delta(b) is some positive constant. #R##N#One of the components of our algorithm is a weak local graph partitioning procedure that may be of independent interest. Given a starting vertex $i$ and a conductance parameter phi, unless a random walk of length ell = O(log n) starting from i mixes rapidly (in terms of phi and ell), we can find a cut of conductance at most phi close to the vertex. The work done per vertex found in the cut is sublinear in n.

Connected through hyperedge HE-1 (induced by source paper "fast distributed approximation for max cut"):
  with members:
    - "efficient algorithms for constructing very sparse spanners and emulators"
    - "a distributed 2 epsilon approximation for vertex cover in o log delta epsilon log log delta rounds"
    - "distributed strong diameter network decomposition"
    - "randomized composable core sets for distributed submodular maximization"
    - "a deterministic almost tight distributed algorithm for approximating single source shortest paths"
    - "a new framework for distributed submodular maximization"
    - "large cuts with local algorithms on triangle free graphs"
    - "distributed approximation algorithms for weighted shortest paths"

Connected through hyperedge HE-2 (induced by source paper "adapting sequential algorithms to the distributed setting"):
  with members:
    - "correlation clustering in data streams"
    - "fast distributed approximation for max cut"
    - "on derandomizing local distributed algorithms"
    - "distributed approximation of maximum independent set and maximum matching"
    - "on the complexity of local distributed graph problems"
    - "optimal dynamic distributed mis"
    - "large cuts with local algorithms on triangle free graphs"
    - "max cut and the smallest eigenvalue"

Connected through hyperedge HE-3 (induced by source paper "streaming lower bounds for approximating max cut"):
  with members:
    - "sketching cuts in graphs and hypergraphs"
    - "access to data and number of iterations dual primal algorithms for maximum matching under resource constraints"
    - "better bounds for matchings in the streaming model"
    - "superlinear lower bounds for multipass graph processing"
    - "linear programming in the semi streaming model with application to the maximum matching problem"
    - "graph sparsification in the semi streaming model"
    - "max cut and the smallest eigenvalue"

Connected through hyperedge HE-4 (induced by source paper "on the power of many one bit provers"):
  with members:
    - "approximation algorithm for non boolean max k csp"
    - "a pcp characterization of am"
    - "approximation resistant predicates from pairwise independence"

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)