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 "explicit polyhedral bounds on network coding rate regions via entropy function region algorithms symmetry and computation"):
  Abstract: The problem of automating rate region converse proofs in multi-source network coding is considered, and an algorithm for explicitly computing polyhedral outer bounds on the rate regions of multi-source network coding instances using known bounds on the entropy function region is proposed. This algorithm is based on the Convex Hull Method, which is an algorithm to compute projections of polyhedra. An interpretation of the network symmetry group as a group of symmetries of a polyhedron is discussed, which in turn enables the use of well-known techniques for exploiting symmetry in polyhedral computation to reduce the complexity of calculating the rate region bounds. A variant of the convex hull method that is capable of exploiting the knowledge of the network symmetry group is discussed. The techniques described in this work are implemented in a software package, called the Information Theoretic Converse Prover. The computational performance of convex hull method for explicitly computing rate region outer bounds with varying knowledge of symmetry is discussed, followed by several examples of computation with the Information Theoretic Converse Prover.

Includes members:
  Member "guessing numbers of odd cycles":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the influence of the interaction graph on a finite dynamical system")
  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 "achievable rate regions for network coding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "characteristic dependent linear rank inequalities via complementary vector spaces")
  Member "non shannon information inequalities in four random variables":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the storage cost of private information retrieval")
  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)