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: "sequential and parallel algorithms for mixed packing and covering"
  Abstract: Mixed packing and covering problems are problems that can be formulated as linear programs using only non-negative coefficients. Examples include multicommodity network flow, the Held-Karp lower bound on TSP, fractional relaxations of set cover, bin-packing, knapsack, scheduling problems, minimum-weight triangulation, etc. This paper gives approximation algorithms for the general class of problems. The sequential algorithm is a simple greedy algorithm that can be implemented to find an epsilon-approximate solution in O(epsilon^-2 log m) linear-time iterations. The parallel algorithm does comparable work but finishes in polylogarithmic time. #R##N#The results generalize previous work on pure packing and covering (the special case when the constraints are all "less-than" or all "greater-than") by Michael Luby and Noam Nisan (1993) and Naveen Garg and Jochen Konemann (1998).

Connected through hyperedge HE-1 (induced by source paper "distributed assignment with limited communication for multi robot multi target tracking"):
  with members:
    - "distributed simultaneous action and target assignment for multi robot multi target tracking"
    - "distributed submodular maximization with limited information"
    - "active target tracking with self triggered communications in multi robot teams"

Connected through hyperedge HE-2 (induced by source paper "a direct tilde o 1 epsilon iteration parallel algorithm for optimal transport"):
  with members:
    - "on efficient optimal transport an analysis of greedy and accelerated mirror descent algorithms"
    - "a graph theoretic additive approximation of optimal transport"
    - "computational optimal transport complexity by accelerated gradient descent is better than by sinkhorn s algorithm"
    - "towards optimal running times for optimal transport"
    - "approximating optimal transport with linear programs"
    - "approximating the quadratic transportation metric in near linear time"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "much faster algorithms for matrix scaling"

Connected through hyperedge HE-3 (induced by source paper "near optimal online algorithms and fast approximation algorithms for resource allocation problems"):
  with members:
    - "fast algorithms for online stochastic convex programming"
    - "primal beats dual on online packing lps in the random order model"
    - "online submodular welfare maximization greedy is optimal"
    - "selective call out and real time bidding"
    - "online stochastic matching online actions based on offline statistics"
    - "online stochastic packing applied to display ad allocation"
    - "a dynamic near optimal algorithm for online linear programming"
    - "online stochastic matching beating 1 1 e"

Connected through hyperedge HE-4 (induced by source paper "improved convergence for ell_ infty and ell_1 regression via iteratively reweighted least squares"):
  with members:
    - "irls and slime mold equivalence and convergence"
    - "using optimization to obtain a width independent parallel simpler and faster positive sdp solver"
    - "on a natural dynamics for linear programming"
    - "using optimization to break the epsilon barrier a faster and simpler width independent algorithm for solving positive linear programs in parallel"
    - "physarum can compute shortest paths"
    - "runtime guarantees for regression problems"
    - "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs"

Connected through hyperedge HE-5 (induced by source paper "no signaling proofs with o log n provers are in pspace"):
  with members:
    - "polynomial space approximation of no signaling provers"

Connected through hyperedge HE-6 (induced by source paper "submodular maximization with matroid and packing constraints in parallel"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "optimal dr submodular maximization and applications to provable mean field inference"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "optimal algorithms for continuous non monotone submodular and dr submodular maximization"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "guaranteed non convex optimization submodular maximization over continuous domains"

Connected through hyperedge HE-7 (induced by source paper "fast and deterministic approximations for k cut"):
  with members:
    - "lp relaxation and tree packing for minimum k cuts"
    - "on approximating sparse covering integer programs"
    - "faster exact and approximate algorithms for k cut"
    - "fast approximations for metric tsp via linear programming"
    - "an fpt algorithm beating 2 approximation for k cut"
    - "approximating the held karp bound for metric tsp in nearly linear time"
    - "deterministic global minimum cut of a simple graph in near linear time"
    - "nearly linear time approximation schemes for mixed packing covering and facility location linear programs"

Connected through hyperedge HE-8 (induced by source paper "submodular function maximization in parallel via the multilinear relaxation"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "submodular optimization in the mapreduce model"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "greed is good near optimal submodular maximization via greedy optimization"
    - "the power of randomization distributed submodular maximization on massive datasets"
    - "streaming algorithms for submodular function maximization"
    - "a new framework for distributed submodular maximization"

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)