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 "on lossless universal compression of distributed identical sources"):
  Abstract: Slepian-Wolf theorem is a well-known framework that targets almost lossless compression of (two) data streams with symbol-by-symbol correlation between the outputs of (two) distributed sources. However, this paper considers a different scenario which does not fit in the Slepian-Wolf framework. We consider two identical but spatially separated sources. 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. Such a scenario occurs, for example, in the universal compression of data from multiple mirrors of the same server. In this setup, the correlation does not arise from symbol-by-symbol dependency of two outputs from the two sources. Instead, the sequences are correlated through the information that they contain about the unknown source parameter. We show that 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 with sequence length $n$. We obtain a lower bound on the average minimax redundancy of almost lossless codes as a function of the sequence length $n$ and the permissible error probability $p_e$ when the decoder has a memory of length $m$ and the encoders do not communicate. Our results demonstrate that a strict performance loss is incurred when the two encoders do not communicate even when the decoder knows the unknown parameter vector (i.e., $m \to \infty$).

Includes members:
  Member "memory assisted universal compression of network flows":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (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-3 (induced by source paper "network compression memory assisted universal coding of sources with correlated parameters")
  Member "results on the redundancy of universal compression for finite length sequences":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "packet level network compression realization and scaling of the network wide benefits")

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)