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: "efficient reassembling of graphs part 1 the linear case"
  Abstract: The reassembling of a simple connected graph$$G = (V,E)$$G=(V,E) is an abstraction of a problem arising in earlier studies of network analysis. Its simplest formulation is in two steps:(1)We cut every edge of G into two halves, thus obtaining a collection of $$n = |\,V\,|$$n=|V| one-vertex components, such that for every $$v\in V$$vźV the one-vertex component $$\{ v \}$${v} has $${{degree}}_{}(v)$$degree(v) half edges attached to it.(2)We splice the two halves of every edge together, not of all the edges at once, but in some ordering $$\Theta $$ź of the edges that minimizes two measures that depend on the edge-boundary degrees of assembled components. A component A is a subset of V and its edge-boundary degree is the number of edges in G with one endpoint in A and one endpoint in $$V-A$$V-A (which is the same as the number of half edges attached to A after all edges with both endpoints in A have been spliced together). The maximum edge-boundary degree encountered during the reassembling process is what we call the $$\varvec{\alpha }$$ź-measure of the reassembling, and the sum of all edge-boundary degrees is its $$\varvec{\beta }$$β-measure. The $$\alpha $$ź-optimization (resp. $$\beta $$β-optimization) of the reassembling of G is to determine an order $$\Theta $$ź for splicing the edges that minimizes its $$\alpha $$ź-measure (resp. $$\beta $$β-measure). There are different forms of reassembling, depending on restrictions and variations on the ordering $$\Theta $$ź of the edges. We consider only cases satisfying the condition that if an edge between disjoint components A and B is spliced, then all the edges between A and B are spliced at the same time. In this report, we examine the particular case of linear reassembling, which requires that the next edge to be spliced must be adjacent to an already spliced edge. We delay other forms of reassembling to follow-up reports. We prove that $$\alpha $$ź-optimization of linear reassembling and minimum-cutwidth linear arrangment ($$\mathrm{CutWidth}$$CutWidth) are polynomially reducible to each other, and that $$\beta $$β-optimization of linear reassembling and minimum-cost linear arrangement ($$\mathrm{MinArr}$$MinArr) are polynomially reducible to each other. The known NP-hardness of $$\mathrm{CutWidth}$$CutWidth and $$\mathrm{MinArr}$$MinArr imply the NP-hardness of $$\alpha $$ź-optimization and $$\beta $$β-optimization.

Connected through hyperedge HE-1 (induced by source paper "efficient reassembling of three regular planar graphs"):
  with members:
    - "a fixed parameter linear time algorithm for maximum flow in planar flow networks"
    - "efficient reassembling of graphs part 2 the balanced case"
    - "a domain specific language for incremental and modular design of large scale verifiably safe flow networks preliminary report"

Connected through hyperedge HE-2 (induced by source paper "efficient reassembling of graphs part 2 the balanced case"):
  with members:
    - "minimum average delay of routing trees"
    - "a domain specific language for incremental and modular design of large scale verifiably safe flow networks preliminary report"
    - "designing multi commodity flow trees"

Connected through hyperedge HE-3 (induced by source paper "minimum average delay of routing trees"):
  with members:
    - "a domain specific language for incremental and modular design of large scale verifiably safe flow networks preliminary report"

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)