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 "beyond the cut set bound uncertainty computations in network coding with correlated sources"):
  Abstract: Cut-set bounds are not, in general, tight for all classes of network communication problems. In this paper, we introduce a new technique for proving converses for the problem of transmission of correlated sources in networks, which results in bounds that are tighter than the corresponding cut-set bounds. We also define the concept of “uncertainty region” which might be of independent interest. We provide a full characterization of this region for the case of two correlated random variables. The bounding technique works as follows: on one hand, we show that if the communication problem is solvable, the uncertainty of certain random variables in the network with respect to imaginary parties that have partial knowledge of the sources must satisfy some constraints that depend on the network architecture. On the other hand, the same uncertainties have to satisfy constraints that only depend on the joint distribution of the sources. Matching these two leads to restrictions on the statistical joint distribution of the sources in communication problems that are solvable over a given network architecture. Our technique also provides nontrivial outer bounds for communication problems with secrecy constraints.

Includes members:
  Member "on secure network coding with uniform wiretap sets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the equivalency of reliability and security metrics for wireline networks")
  Member "beyond the cut set bound uncertainty computations in network coding with correlated sources":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the capacity of computation broadcast")
  Member "multishot codes for network coding using rank metric codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "equivalence and characterizations of linear rank metric codes based on invariants")
  Member "network coding capacity a functional dependence bound":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a minimal set of shannon type inequalities for functional dependence structures")
  Member "packing and covering properties of subspace codes for error control in random linear network coding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "list and probabilistic unique decoding of folded subspace codes")
  Member "a rank metric approach to error control in random network coding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "roos bound for skew cyclic codes in hamming and rank metric")
  Member "coding for errors and erasures in random network coding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the gap between scalar and vector solutions of generalized combination networks")
  Member "packing and covering properties of rank metric codes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multiple rate channel codes in texttt gf p n 2")

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)