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 combinatorial approximation algorithm for graph balancing with light hyper edges"
  Abstract: Makespan minimization in restricted assignment $(R|p_{ij}\in \{p_j, \infty\}|C_{\max})$ is a classical problem in the field of machine scheduling. In a landmark paper in 1990 [8], Lenstra, Shmoys, and Tardos gave a 2-approximation algorithm and proved that the problem cannot be approximated within 1.5 unless P=NP. The upper and lower bounds of the problem have been essentially unimproved in the intervening 25 years, despite several remarkable successful attempts in some special cases of the problem [2,4,12] recently. #R##N#In this paper, we consider a special case called graph-balancing with light hyper edges, where heavy jobs can be assigned to at most two machines while light jobs can be assigned to any number of machines. For this case, we present algorithms with approximation ratios strictly better than 2. Specifically, #R##N#Two job sizes: Suppose that light jobs have weight $w$ and heavy jobs have weight $W$, and $w < W$. We give a $1.5$-approximation algorithm (note that the current 1.5 lower bound is established in an even more restrictive setting [1,3]). Indeed, depending on the specific values of $w$ and $W$, sometimes our algorithm guarantees sub-1.5 approximation ratios. #R##N#Arbitrary job sizes: Suppose that $W$ is the largest given weight, heavy jobs have weights in the range of $(\beta W, W]$, where $4/7\leq \beta < 1$, and light jobs have weights in the range of $(0,\beta W]$. We present a $(5/3+\beta/3)$-approximation algorithm. #R##N#Our algorithms are purely combinatorial, without the need of solving a linear program as required in most other known approaches.

Connected through hyperedge HE-1 (induced by source paper "inapproximability results for scheduling with interval and resource restrictions"):
  with members:
    - "local search breaks 1 75 for graph balancing"
    - "a tale of santa claus hypergraphs and matroids"
    - "restricted max min fair allocation"
    - "structural parameters for scheduling with assignment restrictions"
    - "a quasi polynomial approximation for the restricted assignment problem"
    - "ptas for ordered instances of resource allocation problems with restrictions on inclusions"
    - "graph balancing with two edge types"
    - "combinatorial algorithm for restricted max min fair allocation"

Connected through hyperedge HE-2 (induced by source paper "local search breaks 1 75 for graph balancing"):
  with members:
    - "a quasi polynomial approximation for the restricted assignment problem"
    - "lazy local search meets machine scheduling"
    - "quasi polynomial local search for restricted max min fair allocation"
    - "santa claus schedules jobs on unrelated machines"
    - "on allocating goods to maximize fairness"

Connected through hyperedge HE-3 (induced by source paper "on the configuration lp of the restricted assignment problem"):
  with members:
    - "combinatorial algorithm for restricted max min fair allocation"
    - "quasi polynomial local search for restricted max min fair allocation"
    - "santa claus schedules jobs on unrelated machines"

Connected through hyperedge HE-4 (induced by source paper "graph balancing with two edge types"):
  with members:
    - "the 2 valued case of makespan minimization with assignment constraints"

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)