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 "heterogeneous coded computation across heterogeneous workers"):
  Abstract: Coded distributed computing framework enables large-scale machine learning (ML) models to be trained efficiently in a distributed manner, while mitigating the straggler effect. In this work, we consider a multi-task assignment problem in a coded distributed computing system, where multiple masters, each with a different matrix multiplication task, assign computation tasks to workers with heterogeneous computing capabilities. Both dedicated and probabilistic worker assignment models are considered, with the objective of minimizing the average completion time of all computations. For dedicated worker assignment, greedy algorithms are proposed and the corresponding optimal load allocation is derived based on the Lagrange multiplier method. For probabilistic assignment, successive convex approximation method is used to solve the non-convex optimization problem. Simulation results show that the proposed algorithms reduce the completion time by 80% over uncoded scheme, and 49% over an unbalanced coded scheme.

Includes members:
  Member "computation scheduling for distributed machine learning with straggling workers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "timely distributed computation with stragglers")
  Member "hierarchical coded computation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a locality based approach for coded computation")
  Member "wireless network intelligence at the edge":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adaptive gradient sparsification for efficient federated learning an online learning approach")
  Member "speeding up distributed machine learning using codes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")
  Member "speeding up distributed gradient descent by utilizing non persistent stragglers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "timely distributed computation with stragglers")
  Member "on the effect of task to worker assignment in distributed computing systems with stragglers":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "age of information for updates with distortion constant and age dependent distortion constraints")
  Member "on the optimal recovery threshold of coded matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")
  Member "coded computation over heterogeneous clusters":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")

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)