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 "multicommodity flows and cuts in polymatroidal networks"):
  Abstract: We consider multicommodity flow and cut problems in {\em polymatroidal} networks where there are submodular capacity constraints on the edges incident to a node. Polymatroidal networks were introduced by Lawler and Martel and Hassin in the single-commodity setting and are closely related to the submodular flow model of Edmonds and Giles; the well-known maxflow-mincut theorem holds in this more general setting. Polymatroidal networks for the multicommodity case have not, as far as the authors are aware, been previously explored. Our work is primarily motivated by applications to information flow in wireless networks. We also consider the notion of undirected polymatroidal networks and observe that they provide a natural way to generalize flows and cuts in edge and node capacitated undirected networks. #R##N#We establish poly-logarithmic flow-cut gap results in several scenarios that have been previously considered in the standard network flow models where capacities are on the edges or nodes. Our results have already found aplications in wireless network information flow and we anticipate more in the future. On the technical side our key tools are the formulation and analysis of the dual of the flow relaxations via continuous extensions of submodular functions, in particular the Lov\'asz extension. For directed graphs we rely on a simple yet useful reduction from polymatroidal networks to standard networks. For undirected graphs we rely on the interplay between the Lov\'asz extension of a submodular function and line embeddings with low average distortion introduced by Matousek and Rabinovich; this connection is inspired by, and generalizes, the work of Feige, Hajiaghayi and Lee on node-capacitated multicommodity flows and cuts. The applicability of embeddings to polymatroidal networks is of independent mathematical interest.

Includes members:
  Member "compress and forward scheme for relay networks backword decoding and connection to bisubmodular flows":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "capacity approximations for gaussian relay networks")
  Member "capacity of multiple unicast in wireless networks a polymatroidal approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scalable capacity bounding models for wireless networks")
  Member "wireless network information flow a deterministic approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "best relay selection in gaussian half duplex diamond networks")
  Member "approximation algorithms for submodular multiway partition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improved approximation algorithms for inventory problems")
  Member "submodular cost allocation problem and applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improved approximation algorithms for inventory problems")
  Member "submodular functions extensions distributions and algorithms a survey":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimal approximation for unconstrained non submodular minimization")

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)