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 "width independence beyond linear objectives distributed fair packing and covering algorithms"):
  Abstract: Fair allocation of resources has deep roots in early philosophy, and has been broadly studied in political science, economic theory, operations research, and networking. Over the past decades, an axiomatic approach to fair resource allocation has led to the general acceptance of a class of $\alpha$-fair utility functions parametrized by a single inequality aversion parameter $\alpha \in [0, \infty]$. In theoretical computer science, the most well-studied examples are linear utilities ($\alpha = 0$), proportionally fair or Nash utilities ($\alpha = 1$), and max-min fair utilities ($\alpha \rightarrow \infty$). #R##N#In this work, we consider general $\alpha$-fair resource allocation problems, defined as the maximization of $\alpha$-fair utility functions under packing constraints. We give improved distributed algorithms for constructing $\epsilon$-approximate solutions to such problems. Our algorithms are width-independent, that is, their running times depend only poly-logarithmically on the largest entry of the constraint matrix, and closely match the state-of-the-art guarantees for distributed algorithms for packing linear programs -- the case $\alpha = 0.$ Previously known width-independent algorithms for $\alpha$-fair resource allocation have convergence times with much worse dependence on $\epsilon$ and $\alpha$. Our analysis leverages the Approximate Duality Gap framework of Diakonikolas and Orecchia to obtain better algorithms with a more streamlined analysis. #R##N#Finally, we introduce a natural counterpart of $\alpha$-fairness for minimization problems and motivate its usage in the context of fair task allocation. This generalization yields $\alpha$-fair covering problems, for which we provide the first width-independent nearly-linear-time approximate solvers by reducing their analysis to the $\alpha < 1$ case of the $\alpha$-fair packing problem.

Includes members:
  Member "solving packing and covering lps in tilde o frac 1 epsilon 2 distributed iterations with a single algorithm and simpler analysis":
    also connected through other hyperedges:
      (none)
  Member "fairness in machine learning lessons from political philosophy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "data and model dependencies of membership inference attack")
  Member "a fast distributed stateless algorithm for alpha fair packing problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi path alpha fair resource allocation at scale in distributed software defined networks")
  Member "multi resource fairness objectives algorithms and performance":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "elastic multi resource network slicing can protection lead to improved performance")
  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 "approximating the exponential the lanczos method and an tilde o m time spectral algorithm for balanced separator":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond")
  Member "an axiomatic theory of fairness in network resource allocation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fair resource allocation in federated learning")
  Member "sequential and parallel algorithms for mixed packing and covering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distributed assignment with limited communication for multi robot multi target tracking")

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.DS(Data Structures and Algorithms)