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: "a universal operational theory of unicast multi user communication with fidelity criteria"
  Abstract: This is a three part paper. #R##N#Optimality of source-channel separation for communication with a fidelity criterion when the channel is compound as defined by Csiszar and Korner in their book and general as defined by Verdu and Han, is proved in Part I. It is assumed that random codes are permitted. The word "universal" in the title of this paper refers to the fact that the channel model is compound. The proof uses a layered black-box or a layered input-output view-point. In particular, only the end-to-end description of the channel as being capable of communicating a source to within a certain distortion level is used when proving separation. This implies that the channel model does not play any role for separation to hold as long as there is a source model. Further implications of the layered black-box view-point are discussed. #R##N#Optimality of source-medium separation for multi-user communication with fidelity criteria over a general, compound medium in the unicast setting is proved in Part II, thus generalizing Part I to the unicast, multi-user setting. #R##N#Part III gets to an understanding of the question, "Why is a channel which is capable of communicating a source to within a certain distortion level, also capable of communicating bits at any rate less than the infimum of the rates needed to code the source to within the distortion level": this lies at the heart of why optimality of separation for communication with a fidelity criterion holds. The perspective taken to get to this understanding is a randomized covering-packing perspective, and the proof is operational.

Connected through hyperedge HE-1 (induced by source paper "source broadcasting to the masses separation has a bounded loss"):
  with members:
    - "the approximate capacity of the many to one and one to many gaussian interference channels"
    - "optimality and approximate optimality of source channel separation in networks"
    - "the two user gaussian interference channel a deterministic view"
    - "gaussian interference channel capacity to within one bit"

Connected through hyperedge HE-2 (induced by source paper "information spectrum approach to the source channel separation theorem"):
  with members:
    - "lossy joint source channel coding in the finite blocklength regime"
    - "optimality and approximate optimality of source channel separation in 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)