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: "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 $\alpha \rightarrow \infty$.

Connected through hyperedge HE-1 (induced by source paper "multi path alpha fair resource allocation at scale in distributed software defined networks"):
  with members:
    - "lower bounds for the fair resource allocation problem"
    - "real time fair resource allocation in distributed software defined networks"
    - "asynchronous distributed admm for large scale optimization part i algorithm and convergence analysis"
    - "software defined networking a comprehensive survey"
    - "disco distributed multi domain sdn controllers"
    - "optimization with sparsity inducing penalties"
    - "congestion control with explicit rate indication"

Connected through hyperedge HE-2 (induced by source paper "width independence beyond linear objectives distributed fair packing and covering algorithms"):
  with members:
    - "solving packing and covering lps in tilde o frac 1 epsilon 2 distributed iterations with a single algorithm and simpler analysis"
    - "fairness in machine learning lessons from political philosophy"
    - "multi resource fairness objectives algorithms and performance"
    - "using optimization to break the epsilon barrier a faster and simpler width independent algorithm for solving positive linear programs in parallel"
    - "approximating the exponential the lanczos method and an tilde o m time spectral algorithm for balanced separator"
    - "an axiomatic theory of fairness in network resource allocation"
    - "sequential and parallel algorithms for mixed packing and covering"

Connected through hyperedge HE-3 (induced by source paper "lower bounds for the fair resource allocation problem"):
  with members:
    - "real time fair resource allocation in distributed software defined networks"

Connected through hyperedge HE-4 (induced by source paper "real time fair resource allocation in distributed software defined networks"):
  with members:
    - "software defined networking a comprehensive survey"
    - "disco distributed multi domain sdn controllers"
    - "congestion control with explicit rate indication"

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.DC(Distributed, Parallel, and Cluster Computing)