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: "communication to within a fidelity criterion over unknown networks by reduction to reliable communication problems over unknown networks"
  Abstract: Consider a scenerio where various users want to communicate with each other over an unknown network to within a fidelity criterion. Thus, there are various users. Each user has a source that it wants to send to another user to within some distortion level. We abstract this problem as that of universal communication of random sources over networks to within a distortion criterion. We compute ta universally reliably achievable region for a set of networks where networks in the set are defined in terms of end to end distortion that they achieve for transmission of independent signals between various nodes assuming that there is common randomness between sender and corresponding receiver. Using this, we provide results for when communication of independent signals to within particular fidelity criteria is possible in terms of when reliable communication is possible. Using this, we show that when the sources at the various nodes are independent of each other, it is sufficient to consider separation architectures: architectures consisting of source-coding followed by universal reliable communication. This reduces the problem of communication to within fidelity criterion over networks to that of universal reliable communication. We also consider question of communicating sources between various users to within various fidelity criteria over a set of networks, given an already existing architecture, where there is communication of other sources to within other fidelity criteria. We will not have to break the existing architecture. We also speculate on the possibility of using the approach in this paper to computing capacity of networks.

Connected through hyperedge HE-1 (induced by source paper "hybrid coding an interface for joint source channel coding and network communication"):
  with members:
    - "wireless network information flow a deterministic approach"
    - "on the separation of lossy source network coding and channel coding in wireline networks"
    - "optimality and approximate optimality of source channel separation in networks"
    - "the achievable distortion region of bivariate gaussian source on gaussian broadcast channel"
    - "correlated sources over broadcast channels"
    - "wyner ziv coding over broadcast channels digital schemes"
    - "outer bounds on the admissible source region for broadcast channels with dependent sources"
    - "comments on broadcast channels with arbitrarily correlated sources"

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)