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: "probability of partially decoding network coded messages"
  Abstract: In the literature there exists analytical expressions for the probability of a receiver decoding a transmitted source message that has been encoded using random linear network coding. In this work, we look into the probability that the receiver will decode at least a fraction of the source message, and present an exact solution to this problem for both non-systematic and systematic network coding. Based on the derived expressions, we investigate the potential of these two implementations of network coding for information-theoretic secure communication and progressive recovery of data.

Connected through hyperedge HE-1 (induced by source paper "on the probability of partial decoding in sparse network coding"):
  with members:
    - "reliability of broadcast communications under sparse random linear network coding"
    - "analysis and optimization of sparse random linear network coding for reliable multicast services"
    - "binary systematic network coding for progressive packet decoding"
    - "feedback based online network coding"
    - "intermediate performance of rateless codes"

Connected through hyperedge HE-2 (induced by source paper "secure multi source multicast"):
  with members:
    - "universal secure multiplex network coding with dependent and non uniform messages"
    - "sufficiently myopic adversaries are blind"
    - "probability of partially solving random linear systems in network coding"
    - "can network coding bridge the digital divide in the pacific"
    - "weakly secure regenerating codes for distributed storage"
    - "network coding capacity regions via entropy functions"
    - "universal secure network coding via rank metric codes"
    - "optimal exact regenerating codes for distributed storage at the msr and mbr points via a product matrix construction"

Connected through hyperedge HE-3 (induced by source paper "match made in heaven practical compressed sensing and network coding for intelligent distributed communication networks"):
  with members:
    - "quantized network coding for sparse messages"
    - "a power efficient sensing communication scheme joint source channel network coding by using compressive sensing"

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)