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 "constrained linear representability of polymatroids and algorithms for computing achievability proofs in network coding"):
  Abstract: The constrained linear representability problem (CLRP) for polymatroids determines whether there exists a polymatroid that is linear over a specified field while satisfying a collection of constraints on the rank function. Using a computer to test whether a certain rate vector is achievable with vector linear network codes for a multi-source network coding instance and whether there exists a multi-linear secret sharing scheme achieving a specified information ratio for a given secret sharing instance are shown to be special cases of CLRP. Methods for solving CLRP built from group theoretic techniques for combinatorial generation are developed and described. These techniques form the core of an information theoretic achievability prover, an implementation accompanies the article, and several computational experiments with interesting instances of network coding and secret sharing demonstrating the utility of the method are provided.

Includes members:
  Member "on multi source networks enumeration rate region computation and hierarchy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimization based linear network coding for general connections of continuous flows")
  Member "mapping the region of entropic vectors with support enumeration information geometry":
    also connected through other hyperedges:
      (none)
  Member "characterizing the rate region of the 4 3 3 exact repair regenerating codes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards practical private information retrieval from mds array codes")
  Member "linear network coding linear index coding and representable discrete polymatroids":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "linear network coding effects of varying the message dimension on the set of characteristics")
  Member "path gain algebraic formulation for the scalar linear network coding problem":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a class of non linearly solvable networks")
  Member "a projection method for derivation of non shannon type information inequalities":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "partition symmetrical entropy functions")

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)