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: "optimal function computation in directed and undirected graphs"
  Abstract: We consider the problem of information aggregation in sensor networks, where one is interested in computing a function of the sensor measurements. We allow for block processing and study in-network function computation in directed graphs and undirected graphs. We study how the structure of the function affects the encoding strategies, and the effect of interactive information exchange. #R##N#We begin by considering a directed graph G = (V, E) on the sensor nodes, where the goal is to determine the optimal encoders on each edge which achieve function computation at the collector node. Our goal is to characterize the rate region in R^{|E|}, i.e., the set of points for which there exist feasible encoders with given rates which achieve zero-error computation for asymptotically large block length. We determine the solution for directed trees, specifying the optimal encoder and decoder for each edge. For general directed acyclic graphs, we provide an outer bound on the rate region by finding the disambiguation requirements for each cut, and describe examples where this outer bound is tight. #R##N#Next, we address the scenario where nodes are connected in an undirected tree network, and every node wishes to compute a given symmetric Boolean function of the sensor data. Undirected edges permit interactive computation, and we therefore study the effect of interaction on the aggregation and communication strategies. We focus on sum-threshold functions, and determine the minimum worst-case total number of bits to be exchanged on each edge. The optimal strategy involves recursive in-network aggregation which is reminiscent of message passing. In the case of general graphs, we present a cutset lower bound, and an achievable scheme based on aggregation along trees. For complete graphs, we prove that the complexity of this scheme is no more than twice that of the optimal scheme.

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 "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-3 (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"
    - "linear codes target function classes and network computing capacity"
    - "on network functional compression"
    - "communicating the sum of sources over a network"

Connected through hyperedge HE-4 (induced by source paper "a classification of functions in multiterminal distributed computing"):
  with members:
    - "on distributed computing for functions with certain structures"
    - "distributed function computation over a rooted directed tree"
    - "many help one problem for gaussian sources with a tree structure on their correlation"
    - "the gaussian many help one distributed source coding problem"
    - "rate region of the quadratic gaussian two encoder source coding problem"

Connected through hyperedge HE-5 (induced by source paper "condense a reconfigurable knowledge acquisition architecture for future 5g iot"):
  with members:
    - "network coding as a service"
    - "photonic delay systems as machine learning implementations"
    - "software defined networking a comprehensive survey"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "distributed function computation over a rooted directed tree"
    - "linear coding schemes for the distributed computation of subspaces"
    - "internet of things iot a vision architectural elements and future directions"
    - "on network coding for sum networks"

Connected through hyperedge HE-6 (induced by source paper "rate distortion for lossy in network function computation information dissipation and sequential reverse water filling"):
  with members:
    - "wasserstein continuity of entropy and outer bounds for interference channels"
    - "computing linear transformations with unreliable components"
    - "a generalized cut set bound for deterministic multi flow networks and its applications"
    - "dissipation of information in channels with input constraints"
    - "a generalized typicality for abstract alphabets"
    - "strong data processing inequalities and phi sobolev inequalities for discrete channels"
    - "on cooperation in multi terminal computation and rate distortion"
    - "network coding for computing cut set bounds"

Connected through hyperedge HE-7 (induced by source paper "function computation through a bidirectional relay"):
  with members:
    - "structural properties of index coding capacity using fractional graph theory"
    - "an equivalence between network coding and index coding"
    - "index coding an interference alignment perspective"
    - "on network coding for sum networks"
    - "network coding is zero error always possible"
    - "two way source coding with a helper"
    - "universal coding for correlated sources with complementary delivery"

Connected through hyperedge HE-8 (induced by source paper "on computation rates for arithmetic sum"):
  with members:
    - "sum networks from undirected graphs construction and capacity analysis"
    - "capacity of sum networks for different message alphabets"
    - "upper bound on function computation in directed acyclic networks"
    - "on network coding for sum networks"
    - "network coding for computing cut set bounds"
    - "communicating the sum of sources over a network"

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)