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 "characteristic dependent linear rank inequalities via complementary vector spaces"):
  Abstract: A characteristic-dependent linear rank inequality is a linear inequality that holds by ranks of subspaces of a vector space over a finite field of determined characteristic, and does not in general hold over other characteristics. In this paper, we produce new characteristic-dependent linear rank inequalities by an alternative technique to the usual Dougherty's inverse function method [9]. We take up some ideas of Blasiak [4], applied to certain complementary vector spaces, in order to produce them. Also, we present some applications to network coding. In particular, for each finite or co-finite set of primes $P$, we show that there exists a sequence of networks $\mathcal{N}\left(k\right)$ in which each member is linearly solvable over a field if and only if the characteristic of the field is in $P$, and the linear capacity, over fields whose characteristic is not in $P$, $\rightarrow0$ as $k\rightarrow\infty$.

Includes members:
  Member "characteristic dependent linear rank inequalities with applications to network coding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "how to find new characteristic dependent linear rank inequalities using binary matrices as a guide")
  Member "achievable rate regions for network coding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "how to find new characteristic dependent linear rank inequalities using binary matrices as a guide")
  Member "lexicographic products and the power of non linear network coding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an index coding approach to caching with uncoded cache placement")
  Member "graph theoretical constructions for graph entropy and network coding based communications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "expansive automata networks")
  Member "linear rank inequalities on five or more variables":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "how to find new characteristic dependent linear rank inequalities using binary matrices as a guide")
  Member "broadcasting with side information":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "privacy utility tradeoff in a guessing framework inspired by index coding")

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)