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: "coded computation over heterogeneous clusters"
  Abstract: In large-scale distributed computing clusters, such as Amazon EC2, there are several types of "system noise" that can result in major degradation of performance: system failures, bottlenecks due to limited communication bandwidth, latency due to straggler nodes, etc. On the other hand, these systems enjoy abundance of computing and storage redundancy. There have been recent results that demonstrate the impact of coding for efficient utilization of computation and storage redundancy to alleviate the effect of stragglers and communication bottlenecks in homogeneous clusters. In this paper, we focus on general heterogeneous distributed computing clusters consisting of a variety of computing machines with different capabilities. We propose a coding framework for speeding up distributed computing in heterogeneous clusters by trading redundancy for reducing the latency of computation. In particular, we propose Heterogeneous Coded Matrix Multiplication (HCMM) algorithm for performing distributed matrix multiplication over heterogeneous clusters that is provably asymptotically optimal. Moreover, we show that HCMM is unboundedly faster than uncoded schemes. We also provide numerical results demonstrating significant speedups of up to 90% and 35% for HCMM in comparison to the "uncoded" and "coded homogeneous" schemes, respectively. Furthermore, we carry out real experiments over Amazon EC2 clusters, where HCMM is found to be up to 17% faster than the uncoded scheme. In our worst case experiments with artificial stragglers, HCMM provides speedups of up to 12x over the uncoded scheme. Furthermore, we provide a generalization of the problem of optimal load allocation for heterogeneous clusters to scenarios with budget constraints. In the end, we discuss about the decoding complexity and describe how LDPC codes can be combined with HCMM in order to control the complexity of decoding.

Connected through hyperedge HE-1 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication"):
  with members:
    - "codedprivateml a fast and privacy preserving framework for distributed machine learning"
    - "x secure t private information retrieval from mds coded storage with byzantine and unresponsive servers"
    - "distributed and private coded matrix computation with flexible communication load"
    - "secure coded multi party computation for massive matrix operations"
    - "on the asymptotic capacity of x secure t private information retrieval with graph based replicated storage"
    - "private secure coded computation"
    - "coded matrix multiplication on a group based model"
    - "cross subspace alignment codes for coded distributed batch computation"

Connected through hyperedge HE-2 (induced by source paper "a locality based approach for coded computation"):
  with members:
    - "entangled polynomial codes for secure private and batch distributed matrix multiplication breaking the cubic barrier"
    - "slack squeeze coded computing for adaptive straggler mitigation"
    - "timely throughput optimal coded computing over cloud networks"
    - "train where the data is a case for bandwidth efficient coded training"
    - "collage inference tolerating stragglers in distributed neural network inference using coding"
    - "erasurehead distributed gradient descent without delays using approximate gradient coding"
    - "cross subspace alignment codes for coded distributed batch computation"
    - "hierarchical coded computation"

Connected through hyperedge HE-3 (induced by source paper "numerically stable binary gradient coding"):
  with members:
    - "fundamental limits of approximate gradient coding"
    - "coded distributed computing performance limits and code designs"
    - "gradient coding based on block designs for mitigating adversarial stragglers"
    - "gradient coding with clustering and multi message communication"
    - "distributed stochastic gradient descent using ldgm codes"
    - "universally decodable matrices for distributed matrix vector multiplication"
    - "erasurehead distributed gradient descent without delays using approximate gradient coding"
    - "speeding up distributed machine learning using codes"

Connected through hyperedge HE-4 (induced by source paper "coded computing for boolean functions"):
  with members:
    - "codedprivateml a fast and privacy preserving framework for distributed machine learning"
    - "slack squeeze coded computing for adaptive straggler mitigation"
    - "secure coded multi party computation for massive matrix operations"
    - "timely throughput optimal coded computing over cloud networks"
    - "gradient coding based on block designs for mitigating adversarial stragglers"
    - "hierarchical coded computation"
    - "speeding up distributed machine learning using codes"
    - "lagrange coded computing optimal design for resiliency security and privacy"

Connected through hyperedge HE-5 (induced by source paper "coded federated learning"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "polynomially coded regression optimal straggler mitigation via data encoding"
    - "federated learning with non iid data"

Connected through hyperedge HE-6 (induced by source paper "parity models a general framework for coding based resilience in ml inference"):
  with members:
    - "collage inference tolerating stragglers in distributed neural network inference using coding"
    - "pretzel opening the black box of machine learning prediction serving systems"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning a code machine learning for approximate non linear coded computation"
    - "leveraging coding techniques for speeding up distributed computing"
    - "speeding up distributed machine learning using codes"
    - "rafiki machine learning as an analytics service system"
    - "tvm an automated end to end optimizing compiler for deep learning"

Connected through hyperedge HE-7 (induced by source paper "codenet training large scale neural networks in presence of soft errors"):
  with members:
    - "accumulation bit width scaling for ultra low precision training of deep networks"
    - "deep neural network approximation for custom hardware where we ve been where we re going"
    - "an application of storage optimal matdot codes for coded matrix multiplication fast k nearest neighbors estimation"
    - "hierarchical coded computation"
    - "learning a code machine learning for approximate non linear coded computation"
    - "speeding up distributed machine learning using codes"
    - "straggler resilient and communication efficient distributed iterative linear solver"
    - "training deep neural networks with 8 bit floating point numbers"

Connected through hyperedge HE-8 (induced by source paper "cross subspace alignment codes for coded distributed batch computation"):
  with members:
    - "x secure t private information retrieval from mds coded storage with byzantine and unresponsive servers"
    - "on the capacity of secure distributed matrix multiplication"
    - "on the asymptotic capacity of x secure t private information retrieval with graph based replicated storage"
    - "coded matrix multiplication on a group based model"
    - "an application of storage optimal matdot codes for coded matrix multiplication fast k nearest neighbors estimation"
    - "speeding up distributed machine learning using codes"
    - "rate efficiency and straggler robustness through partition in distributed two sided secure matrix computation"
    - "cross subspace alignment and the asymptotic capacity of x secure t private information retrieval"

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.DC(Distributed, Parallel, and Cluster Computing)