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 "optimality and approximate optimality of source channel separation in networks"):
  Abstract: We consider the source-channel separation architecture for lossy source coding in communication networks. It is shown that the separation approach is optimal in two general scenarios, and is approximately optimal in a third scenario. The two scenarios for which separation is optimal complement each other: the first is when the memoryless sources at source nodes are arbitrarily correlated, each of which is to be reconstructed at possibly multiple destinations within certain distortions, but the channels in this network are synchronized, orthogonal and memoryless point-to-point channels; the second is when the memoryless sources are mutually independent, each of which is to be reconstructed only at one destination within a certain distortion, but the channels are general, including multi-user channels such as multiple access, broadcast, interference and relay channels, possibly with feedback. The third scenario, for which we demonstrate approximate optimality of source-channel separation, generalizes the second scenario by allowing each source to be reconstructed at multiple destinations with different distortions. For this case, the loss from optimality by using the separation approach can be upper-bounded when a "difference" distortion measure is taken, and in the special case of quadratic distortion measure, this leads to universal constant bounds.

Includes members:
  Member "separation of source network coding and channel coding in wireline networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scalable capacity bounding models for wireless networks")
  Member "on the separation of lossy source network coding and channel coding in wireline networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "constrained source coding with side information")
  Member "cascade and triangular source coding with side information at the first two nodes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hypothesis testing over the two hop relay network")
  Member "cascade multiterminal source coding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hypothesis testing over the two hop relay network")
  Member "hybrid coding for gaussian broadcast channels with gaussian sources":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed source simulation with no communication")
  Member "approximate characterizations for the gaussian source broadcast distortion region":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distortion bounds for source broadcast over degraded channel")
  Member "multicasting correlated multi source to multi sink over a network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "polymatroids with network coding")
  Member "on multistage successive refinement for wyner ziv source coding with degraded side informations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "vector gaussian successive refinement with degraded side information")

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)