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: "a storage computation communication tradeoff for distributed computing"
  Abstract: This paper investigates distributed computing systems where computations are split into "Map" and "Reduce" functions. A new coded scheme, called distributed computing and coded communication (D3C), is proposed, and its communication load is analyzed as a function of the available storage space and the number of intermediate values (IVA) to be computed. D3C achieves the smallest possible communication load for a given storage space, while a smaller number of IVAs need to be computed compared to Li \emph{et al.'}s coded distributed computing (CDC) scheme. More generally, our scheme can flexibly trade between storage space and the number of IVAs to be computed. Communication load is then analyzed for any given tradeoff.

Connected through hyperedge HE-1 (induced by source paper "a fundamental storage communication tradeoff in distributed computing with straggling nodes"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "storage computation and communication a fundamental tradeoff in distributed computing"
    - "speeding up distributed gradient descent by utilizing non persistent stragglers"
    - "hierarchical coding for distributed computing"
    - "placement delivery array design through strong edge coloring of bipartite graphs"
    - "coded distributed computing with node cooperation substantially increases speedup factors"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "improved latency communication trade off for map shuffle reduce systems with stragglers"

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"
    - "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 mapreduce distributed computing"
    - "wireless map reduce distributed computing with full duplex radios and imperfect csi"

Connected through hyperedge HE-3 (induced by source paper "storage computation and communication a fundamental tradeoff in distributed computing"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "hierarchical coding for distributed computing"
    - "placement delivery array design through strong edge coloring of bipartite graphs"
    - "coded distributed computing with node cooperation substantially increases speedup factors"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "communication vs distributed computation an alternative trade off curve"
    - "on the placement delivery array design for centralized coded caching scheme"

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"
    - "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"
    - "wireless mapreduce distributed computing"
    - "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)