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 network functional compression"):
  Abstract: In this paper, we consider different aspects of the network functional compression problem where computation of a function (or, some functions) of sources located at certain nodes in a network is desired at receiver(s). The rate region of this problem has been considered in the literature under certain restrictive assumptions, particularly in terms of the network topology, the functions and the characteristics of the sources. In this paper, we present results that significantly relax these assumptions. Firstly, we consider this problem for an arbitrary tree network and asymptotically lossless computation. We show that, for depth one trees with correlated sources, or for general trees with independent sources, a modularized coding scheme based on graph colorings and Slepian-Wolf compression performs arbitrarily closely to rate lower bounds. For a general tree network with independent sources, optimal computation to be performed at intermediate nodes is derived. We introduce a necessary and sufficient condition on graph colorings of any achievable coding scheme, called coloring connectivity condition (C.C.C.). #R##N#Secondly, we investigate the effect of having several functions at the receiver. In this problem, we derive a rate region and propose a coding scheme based on graph colorings. Thirdly, we consider the functional compression problem with feedback. We show that, in this problem, unlike Slepian-Wolf compression, by having feedback, one may outperform rate bounds of the case without feedback. Fourthly, we investigate functional computation problem with distortion. We compute a rate-distortion region for this problem. Then, we propose a simple suboptimal coding scheme with a non-trivial performance guarantee. Finally, we introduce cases where finding minimum entropy colorings and therefore, optimal coding schemes can be performed in polynomial time.

Includes members:
  Member "network coding for computing cut set bounds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "expand and randomize an algebraic approach to secure computation")
  Member "network flows for functions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "streaming big data meets backpressure in distributed network computation")
  Member "optimal computation of symmetric boolean functions in tree networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "how to distribute computation in networks")
  Member "on zero error source coding with feedback":
    also connected through other hyperedges:
      (none)
  Member "information theoretic bounds for multiround function computation in collocated networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed function computation over a rooted directed tree")
  Member "distributed source coding for interactive function computation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "round complexity of common randomness generation the amortized setting")
  Member "on distributed function computation in structure free random networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "how to distribute computation in networks")
  Member "rate region of the quadratic gaussian two encoder source coding problem":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "integer forcing architectures for uplink cloud radio access 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)