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 "network compression memory assisted universal coding of sources with correlated parameters"):
  Abstract: In this paper, we propose {\em distributed network compression via memory}. We consider two spatially separated sources with correlated unknown source parameters. We wish to study the universal compression of a sequence of length $n$ from one of the sources provided that the decoder has access to (i.e., memorized) a sequence of length $m$ from the other source. In this setup, the correlation does not arise from symbol-by-symbol dependency of two outputs from the two sources (as in Slepian-Wolf setup). Instead, the two sequences are correlated because they are originated from the two sources with \emph{unknown} correlated parameters. The finite-length nature of the compression problem at hand requires considering a notion of almost lossless source coding, where coding incurs an error probability $p_e(n)$ that vanishes as sequence length $n$ grows to infinity. We obtain bounds on the redundancy of almost lossless codes when the decoder has access to a random memory of length $m$ as a function of the sequence length $n$ and the permissible error probability $p_e(n)$. Our results demonstrate that distributed network compression via memory has the potential to significantly improve over conventional end-to-end compression when sufficiently large memory from previous communications is available to the decoder.

Includes members:
  Member "on lossless universal compression of distributed identical sources":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "universal compression of a mixture of parametric sources with side information")
  Member "memory assisted universal compression of network flows":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "packet level network compression realization and scaling of the network wide benefits")
  Member "results on the fundamental gain of memory assisted universal source coding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "universal compression with side information from a correlated source")
  Member "memory assisted universal source coding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on lossless universal compression of distributed identical sources")
  Member "results on the redundancy of universal compression for finite length sequences":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "universal compression with side information from a correlated source")
  Member "on the network wide gain of memory assisted source coding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "packet level network compression realization and scaling of the network wide benefits")
  Member "femtocell networks a survey":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robust resource allocation for pd noma based miso heterogeneous networks with comp technology")
  Member "uplink capacity and interference avoidance for two tier femtocell networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a brief history on the theoretical analysis of dense small cell wireless networks")

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)