human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "performance bounds on a wiretap network with arbitrary wiretap sets"
  Abstract: Consider a communication network represented by a directed graph $\mathcal{G}=(\mathcal{V},\mathcal{E})$, where $\mathcal{V}$ is the set of nodes and $\mathcal{E}$ is the set of point-to-point channels in the network. On the network a secure message $M$ is transmitted, and there may exist wiretappers who want to obtain information about the message. In secure network coding, we aim to find a network code which can protect the message against the wiretapper whose power is constrained. Cai and Yeung \cite{cai2002secure} studied the model in which the wiretapper can access any one but not more than one set of channels, called a wiretap set, out of a collection $\mathcal{A}$ of all possible wiretap sets. In order to protect the message, the message needs to be mixed with a random key $K$. They proved tight fundamental performance bounds when $\mathcal{A}$ consists of all subsets of $\mathcal{E}$ of a fixed size $r$. However, beyond this special case, obtaining such bounds is much more difficult. In this paper, we investigate the problem when $\mathcal{A}$ consists of arbitrary subsets of $\mathcal{E}$ and obtain the following results: 1) an upper bound on $H(M)$; 2) a lower bound on $H(K)$ in terms of $H(M)$. The upper bound on $H(M)$ is explicit, while the lower bound on $H(K)$ can be computed in polynomial time when $|\mathcal{A}|$ is fixed. The tightness of the lower bound for the point-to-point communication system is also proved.

Connected through hyperedge HE-1 (induced by source paper "local encoding preserving secure network coding part i fixed security level"):
  with members:
    - "alphabet size reduction for secure network coding a graph theoretic approach"
    - "on the optimality of secure network coding"
    - "variable rate linear network error correction mds codes"
    - "on secure network coding with nonuniform or restricted wiretap sets"
    - "universal secure network coding via rank metric codes"
    - "network codes resilient to jamming and eavesdropping"

Connected through hyperedge HE-2 (induced by source paper "on the equivalency of reliability and security metrics for wireline networks"):
  with members:
    - "perfectly secure index coding"
    - "graph theory versus minimum rank for index coding"
    - "using feedback for secrecy over graphs"
    - "on the capacity region for index coding"
    - "on secure network coding with nonuniform or restricted wiretap sets"
    - "on secure network coding with uniform wiretap sets"
    - "achievability proof via output statistics of random binning"
    - "dynamic index coding for wireless broadcast networks"

Connected through hyperedge HE-3 (induced by source paper "a numerical study on the wiretap network with a simple network topology"):
  with members:
    - "connecting multiple unicast and network error correction reduction and unachievability"
    - "characterizing the rate region of the 4 3 3 exact repair regenerating codes"
    - "on secure network coding with nonuniform or restricted wiretap sets"
    - "on secure network coding with uniform wiretap sets"
    - "network coding capacity regions via entropy functions"
    - "linear rank inequalities on five or more variables"

Connected through hyperedge HE-4 (induced by source paper "on the optimality of secure network coding"):
  with members:
    - "small field size for secure network coding"

Question: Please tell me which class the center node 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)