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: "on 1 epsilon restricted max min fair allocation problem"
  Abstract: We study the max-min fair allocation problem in which a set of $m$ indivisible items are to be distributed among $n$ agents such that the minimum utility among all agents is maximized. In the restricted setting, the utility of each item $j$ on agent $i$ is either $0$ or some non-negative weight $w_j$. For this setting, Asadpour et al. showed that a certain configuration-LP can be used to estimate the optimal value within a factor of $4+\delta$, for any $\delta>0$, which was recently extended by Annamalai et al. to give a polynomial-time $13$-approximation algorithm for the problem. For hardness results, Bezakova and Dani showed that it is \NP-hard to approximate the problem within any ratio smaller than $2$. #R##N#In this paper we consider the $(1,\epsilon)$-restricted max-min fair allocation problem in which each item $j$ is either heavy $(w_j = 1)$ or light $(w_j = \epsilon)$, for some parameter $\epsilon \in (0,1)$. We show that the $(1,\epsilon)$-restricted case is also \NP-hard to approximate within any ratio smaller than $2$. Hence, this simple special case is still algorithmically interesting. #R##N#Using the configuration-LP, we are able to estimate the optimal value of the problem within a factor of $3+\delta$, for any $\delta>0$. Extending this idea, we also obtain a quasi-polynomial time $(3+4\epsilon)$-approximation algorithm and a polynomial time $9$-approximation algorithm. Moreover, we show that as $\epsilon$ tends to $0$, the approximation ratio of our polynomial-time algorithm approaches $3+2\sqrt{2}\approx 5.83$.

Connected through hyperedge HE-1 (induced by source paper "integrality gap of the configuration lp for the restricted max min fair allocation"):
  with members:
    - "restricted max min fair allocation"
    - "a new approximation technique for resource allocation problems"
    - "santa claus schedules jobs on unrelated machines"

Connected through hyperedge HE-2 (induced by source paper "restricted max min fair allocation"):
  with members:
    - "a new approximation technique for resource allocation problems"
    - "new constructive aspects of the lovasz local lemma"
    - "on allocating goods to maximize fairness"

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.DM(Discrete Mathematics)