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 "a fast distributed stateless algorithm for alpha fair packing problems"):
  Abstract: Over the past two decades, fair resource allocation problems have received considerable attention in a variety of application areas. However, little progress has been made in the design of distributed algorithms with convergence guarantees for general and commonly used $\alpha$-fair allocations. In this paper, we study weighted $\alpha$-fair packing problems, that is, the problems of maximizing the objective functions (i) $\sum_j w_j x_j^{1-\alpha}/(1-\alpha)$ when $\alpha > 0$, $\alpha \neq 1$ and (ii) $\sum_j w_j \ln x_j$ when $\alpha = 1$, over linear constraints $Ax \leq b$, $x\geq 0$, where $w_j$ are positive weights and $A$ and $b$ are non-negative. We consider the distributed computation model that was used for packing linear programs and network utility maximization problems. Under this model, we provide a distributed algorithm for general $\alpha$ that converges to an $\varepsilon-$approximate solution in time (number of distributed iterations) that has an inverse polynomial dependence on the approximation parameter $\varepsilon$ and poly-logarithmic dependence on the problem size. This is the first distributed algorithm for weighted $\alpha-$fair packing with poly-logarithmic convergence in the input size. The algorithm uses simple local update rules and is stateless (namely, it allows asynchronous updates, is self-stabilizing, and allows incremental and local adjustments). We also obtain a number of structural results that characterize $\alpha-$fair allocations as the value of $\alpha$ is varied. These results deepen our understanding of fairness guarantees in $\alpha-$fair packing allocations, and also provide insight into the behavior of $\alpha-$fair allocations in the asymptotic cases $\alpha\rightarrow 0$, $\alpha \rightarrow 1$, and $\alpha \rightarrow \infty$.

Includes members:
  Member "max min fair rate allocation and routing in energy harvesting networks algorithmic analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a fast distributed algorithm for α fair packing problems")
  Member "multi resource fairness objectives algorithms and performance":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "elastic multi resource network slicing can protection lead to improved performance")
  Member "competitive algorithms from competitive equilibria non clairvoyant scheduling under polyhedral constraints":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dynamic weighted fairness with minimal disruptions")
  Member "using optimization to break the epsilon barrier a faster and simpler width independent algorithm for solving positive linear programs in parallel":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improved convergence for ell_ infty and ell_1 regression via iteratively reweighted least squares")
  Member "on line end to end congestion control":
    also connected through other hyperedges:
      (none)
  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 "congestion control with explicit rate indication":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi path alpha fair resource allocation at scale in distributed software defined networks")

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.DC(Distributed, Parallel, and Cluster Computing)