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: "wireless mapreduce distributed computing"
  Abstract: Motivated by mobile edge computing and wireless data centers, we study a wireless distributed computing framework where the distributed nodes exchange information over a wireless interference network. Following the structure of MapReduce, this framework consists of Map, Shuffle, and Reduce phases, where Map and Reduce are computation phases and Shuffle is a data transmission phase operated over a wireless interference network. By duplicating the computation work at a cluster of distributed nodes in the Map phase, one can reduce the amount of transmission load required for the Shuffle phase. In this work, we characterize the fundamental tradeoff between computation load and communication load, under the assumption of one-shot linear schemes. The proposed scheme is based on side information cancellation and zero-forcing, and turns out to be optimal. The proposed scheme outperforms the naive TDMA scheme with single node transmission at a time, as well as the coded TDMA scheme that allows coding across data, in terms of the computation-communication tradeoff.

Connected through hyperedge HE-1 (induced by source paper "on the optimality of d2d coded caching with uncoded cache placement and one shot delivery"):
  with members:
    - "the exact rate memory tradeoff for caching with uncoded prefetching"
    - "speeding up distributed machine learning using codes"
    - "a pliable index coding approach to data shuffling"
    - "coded computation over heterogeneous clusters"
    - "characterizing the rate memory tradeoff in cache networks within a factor of 2"
    - "coded caching in partially cooperative d2d communication networks"
    - "how to optimally allocate resources for coded distributed computing"
    - "minimizing latency for secure distributed computing"

Connected through hyperedge HE-2 (induced by source paper "coded federated computing in wireless networks with straggling devices and imperfect csi"):
  with members:
    - "wireless network intelligence at the edge"
    - "speeding up distributed machine learning using codes"
    - "federated learning strategies for improving communication efficiency"
    - "a storage computation communication tradeoff for distributed computing"
    - "wireless data center networks advances challenges and opportunities"
    - "improved latency communication trade off for map shuffle reduce systems with stragglers"
    - "on the optimal recovery threshold of coded matrix multiplication"
    - "wireless map reduce distributed computing with full duplex radios and imperfect csi"

Connected through hyperedge HE-3 (induced by source paper "heterogeneous coded distributed computing joint design of file allocation and function assignment"):
  with members:
    - "coded distributed computing with heterogeneous function assignments"
    - "cascaded coded distributed computing on heterogeneous networks"
    - "leveraging coding techniques for speeding up distributed computing"
    - "storage computation and communication a fundamental tradeoff in distributed computing"
    - "a new combinatorial design of coded distributed computing"
    - "on heterogeneous coded distributed computing"
    - "a fundamental tradeoff between computation and communication in distributed computing"
    - "a scalable framework for wireless distributed computing"

Connected through hyperedge HE-4 (induced by source paper "wireless map reduce distributed computing with full duplex radios and imperfect csi"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "speeding up distributed gradient descent by utilizing non persistent stragglers"
    - "a storage computation communication tradeoff for distributed computing"
    - "coded distributed computing with node cooperation substantially increases speedup factors"
    - "data shuffling in wireless distributed computing via low rank optimization"
    - "improved latency communication trade off for map shuffle reduce systems with stragglers"
    - "cloud edge non orthogonal transmission for fog networks with delayed csi at the cloud"
    - "fog aided wireless networks for content delivery fundamental latency tradeoffs"

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)