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 numerical study on the wiretap network with a simple network topology"):
  Abstract: In this paper, we study a security problem on a simple wiretap network, consisting of a source node S, a destination node D, and an intermediate node R. The intermediate node connects the source and the destination nodes via a set of noiseless parallel channels, with sizes $n_1$ and $n_2$, respectively. A message $M$ is to be sent from S to D. The information in the network may be eavesdropped by a set of wiretappers. The wiretappers cannot communicate with one another. Each wiretapper can access a subset of channels, called a wiretap set. All the chosen wiretap sets form a wiretap pattern. A random key $K$ is generated at S and a coding scheme on $(M, K)$ is employed to protect $M$. We define two decoding classes at D: In Class-I, only $M$ is required to be recovered and in Class-II, both $M$ and $K$ are required to be recovered. The objective is to minimize $H(K)/H(M)$ {for a given wiretap pattern} under the perfect secrecy constraint. The first question we address is whether routing is optimal on this simple network. By enumerating all the wiretap patterns on the Class-I/II $(3,3)$ networks and harnessing the power of Shannon-type inequalities, we find that gaps exist between the bounds implied by routing and the bounds implied by Shannon-type inequalities for a small fraction~($<2\%$) of all the wiretap patterns. The second question we investigate is the following: What is $\min H(K)/H(M)$ for the remaining wiretap patterns where gaps exist? We study some simple wiretap patterns and find that their Shannon bounds (i.e., the lower bound induced by Shannon-type inequalities) can be achieved by linear codes, which means routing is not sufficient even for the ($3$, $3$) network. For some complicated wiretap patterns, we study the structures of linear coding schemes under the assumption that they can achieve the corresponding Shannon bounds....

Includes members:
  Member "connecting multiple unicast and network error correction reduction and unachievability":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "arbitrarily varying networks capacity achieving computationally efficient codes")
  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 "on secure network coding with nonuniform or restricted wiretap sets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "network information theoretic security")
  Member "on secure network coding with uniform wiretap sets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the equivalency of reliability and security metrics for wireline networks")
  Member "network coding capacity regions via entropy functions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a local perspective on the edge removal problem")
  Member "performance bounds on a wiretap network with arbitrary wiretap sets":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "local encoding preserving secure network coding part i fixed security level")
  Member "linear rank inequalities on five or more variables":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "characteristic dependent linear rank inequalities via complementary vector spaces")

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)