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: "near linear time approximation schemes for uncapacitated and capacitated b matching problems in nonbipartite graphs"
  Abstract: We present the first near optimal approximation schemes for the #R##N#maximum weighted (uncapacitated or capacitated) $b$--matching #R##N#problems for non-bipartite graphs that run in time (near) linear in #R##N#the number of edges. For any $\delta>3/\sqrt{n}$ the algorithm #R##N#produces a $(1-\delta)$ approximation in $O(m \poly(\delta^{-1},\log #R##N#n))$ time. We provide fractional solutions for the standard linear #R##N#programming formulations for these problems and subsequently also #R##N#provide (near) linear time approximation schemes #R##N#for rounding the fractional solutions. #R##N#Through these problems as a vehicle, we also present several ideas #R##N#in the context of solving linear programs approximately using fast #R##N#primal-dual algorithms. First, even though the dual of these #R##N#problems have exponentially many variables and an efficient exact #R##N#computation of dual weights is infeasible, we show that we can #R##N#efficiently compute and use a sparse approximation of the dual #R##N#weights using a combination of (i) adding perturbation to the #R##N#constraints of the polytope and (ii) amplification followed by #R##N#thresholding of the dual weights. Second, we show that #R##N#approximation algorithms can be used to reduce the width of the #R##N#formulation, and faster convergence.

Connected through hyperedge HE-1 (induced by source paper "design of dynamic algorithms via primal dual method"):
  with members:
    - "deterministic fully dynamic data structures for vertex cover and matching"
    - "simple deterministic algorithms for fully dynamic maximal matching"
    - "fully dynamic maximal matching in o log n update time"

Connected through hyperedge HE-2 (induced by source paper "access to data and number of iterations dual primal algorithms for maximum matching under resource constraints"):
  with members:
    - "a local computation approximation scheme to maximum matching"
    - "better bounds for matchings in the streaming model"
    - "lower bounds for adaptive sparse recovery"
    - "superlinear lower bounds for multipass graph processing"
    - "laminar families and metric embeddings non bipartite maximum matching problem in the semi streaming model"
    - "improved approximation guarantees for weighted matching in the semi streaming model"
    - "linear programming in the semi streaming model with application to the maximum matching problem"
    - "weighted matching in the semi streaming model"

Connected through hyperedge HE-3 (induced by source paper "approximating semi matchings in streaming and in two party communication"):
  with members:
    - "single pass spectral sparsification in dynamic streams"
    - "better bounds for matchings in the streaming model"
    - "superlinear lower bounds for multipass graph processing"
    - "maximum matching in semi streaming with few passes"
    - "linear programming in the semi streaming model with application to the maximum matching problem"
    - "faster algorithms for semi matching 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)