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: "universal coding for correlated sources with complementary delivery"
  Abstract: This paper deals with a universal coding problem for a certain kind of multiterminal source coding system that we call the complementary delivery coding system. In this system, messages from two correlated sources are jointly encoded, and each decoder has access to one of the two messages to enable it to reproduce the other message. Both fixed-to-fixed length and fixed-to-variable length lossless coding schemes are considered. Explicit constructions of universal codes and bounds of the error probabilities are clarified via type-theoretical and graph-theoretical analyses.

Connected through hyperedge HE-1 (induced by source paper "function computation through a bidirectional relay"):
  with members:
    - "structural properties of index coding capacity using fractional graph theory"
    - "an equivalence between network coding and index coding"
    - "index coding an interference alignment perspective"
    - "on network coding for sum networks"
    - "network coding is zero error always possible"
    - "optimal function computation in directed and undirected graphs"
    - "two way source coding with a helper"

Connected through hyperedge HE-2 (induced by source paper "broadcast function computation with complementary side information"):
  with members:
    - "structural properties of index coding capacity using fractional graph theory"
    - "zero error function computation through a bidirectional relay"
    - "an equivalence between network coding and index coding"
    - "index coding an interference alignment perspective"
    - "network coding is zero error always possible"

Connected through hyperedge HE-3 (induced by source paper "universal source coding over generalized complementary delivery networks"):
  with members:
    - "universal coding for lossless and lossy complementary delivery problems"
    - "network information flow with correlated sources"

Connected through hyperedge HE-4 (induced by source paper "universal coding for lossless and lossy complementary delivery problems"):
  with members:
    - "universal source coding over generalized complementary delivery networks"
    - "multiterminal source coding with complementary delivery"
    - "on the wyner ziv problem for individual sequences"

Connected through hyperedge HE-5 (induced by source paper "multiterminal source coding with complementary delivery"):
  with members:
    - "universal source coding over generalized complementary delivery networks"

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)