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 "a combinatorial study of linear deterministic relay networks"):
  Abstract: In the last few years the so--called "linear deterministic" model of relay channels has gained popularity as a means of studying the flow of information over wireless communication networks, and this approach generalizes the model of wireline networks which is standard in network optimization. There is recent work extending the celebrated max--flow/min--cut theorem to the capacity of a unicast session over a linear deterministic relay network which is modeled by a layered directed graph. This result was first proved by a random coding scheme over large blocks of transmitted signals. We demonstrate the same result with a simple, deterministic, polynomial--time algorithm which takes as input a single transmitted signal instead of a long block of signals. Our capacity-achieving transmission scheme for a two--layer network requires the extension of a one--dimensional Rado--Hall transversal theorem on the independent subsets of rows of a row--partitioned matrix into a two--dimensional variation for block matrices. To generalize our approach to larger networks we use the submodularity of the capacity of a cut for our model and show that our complete transmission scheme can be obtained by solving a linear program over the intersection of two polymatroids. We prove that our transmission scheme can achieve the max-flow/min-cut capacity by applying a theorem of Edmonds about such linear programs. We use standard submodular function minimization techniques as part of our polynomial--time algorithm to construct our capacity-achieving transmission scheme.

Includes members:
  Member "approximate capacity of gaussian relay networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multihop backhaul compression for the uplink of cloud radio access networks")
  Member "transmission techniques for relay interference networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cascade codes for distributed storage systems")
  Member "a deterministic approach to wireless relay networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the deterministic sum capacity of the multiple access wiretap channel")
  Member "wireless network information flow":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "perfect output feedback in the two user decentralized interference channel")

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.IT(Information Theory)