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: "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.

Connected through hyperedge HE-1 (induced by source paper "how to distribute computation in networks"):
  with members:
    - "on the derivative imbalance and ambiguity of functions"
    - "the exact rate memory tradeoff for caching with uncoded prefetching"
    - "lagrange coded computing optimal design for resiliency security and privacy"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "on heterogeneous coded distributed computing"
    - "a fundamental tradeoff between computation and communication in distributed computing"
    - "comments on cut set bounds on network function computation"
    - "computing linear functions by linear coding over networks"

Connected through hyperedge HE-2 (induced by source paper "functional epsilon entropy"):
  with members:
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"
    - "polarization for arbitrary discrete memoryless channels"

Connected through hyperedge HE-3 (induced by source paper "zero error function computation on a directed acyclic network"):
  with members:
    - "improved upper bound on the network function computing capacity"
    - "on computation rates for arithmetic sum"
    - "sum networks from incidence structures construction and capacity analysis"
    - "upper bound on function computation in directed acyclic networks"
    - "on the multiple unicast capacity of 3 source 3 terminal directed acyclic networks"
    - "distributed function computation over a rooted directed tree"
    - "on network coding for sum networks"
    - "network coding for computing cut set bounds"

Connected through hyperedge HE-4 (induced by source paper "improved upper bound on the network function computing capacity"):
  with members:
    - "upper bound on function computation in directed acyclic networks"
    - "on network coding for sum networks"
    - "computing linear functions by linear coding over networks"
    - "network coding for computing cut set bounds"
    - "optimal function computation in directed and undirected graphs"
    - "linear codes target function classes and network computing capacity"
    - "communicating the sum of sources over a network"

Connected through hyperedge HE-5 (induced by source paper "graph codes for distributed instant message collection in an arbitrary noisy broadcast network"):
  with members:
    - "sum networks from undirected graphs construction and capacity analysis"
    - "energy efficient distributed coding for data collection in a noisy sparse network"
    - "computing linear transformations with unreliable components"
    - "update efficiency and local repairability limits for capacity approaching codes"
    - "computation over gaussian networks with orthogonal components"
    - "computing linear functions by linear coding over networks"
    - "network coding for computing cut set bounds"
    - "towards a queueing based framework for in network function computation"

Connected through hyperedge HE-6 (induced by source paper "robust coding for lossy computing with receiver side observation costs"):
  with members:
    - "robust coding for lossy computing with observation costs"
    - "source coding with a side information vending machine"

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)