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 computing for distributed graph analytics"
  Abstract: Many distributed graph computing systems have been developed recently for efficient processing of massive graphs. These systems require many messages to be exchanged among computing machines at each step of the computation, making communication bandwidth a major performance bottleneck. We present a coded computing framework that systematically injects redundancy in the computation phase to enable coding opportunities in the communication phase thus reducing the communication load substantially. Specifically, we propose coded schemes that enable an inverse-linear trade-off (asymptotically) between computation load and average communication load for three popular random graphs -- Erd\"os-R\'enyi (ER), random bi-partite (RB), stochastic block model (SBM). The proposed scheme for ER graph is shown to be optimal asymptotically as the graph size $n\rightarrow \infty$. For finite $n$, we demonstrate via numerical analysis that for a given computation load $r$, i.e. when each graph node is carefully stored at $r$ servers, the proposed scheme slashes the average communication load by (nearly) $r$.

Connected through hyperedge HE-1 (induced by source paper "coded distributed computing schemes with smaller numbers of input files and output functions"):
  with members:
    - "resolvable designs for speeding up distributed computing"
    - "speeding up distributed machine learning using codes"
    - "storage computation and communication a fundamental tradeoff in distributed computing"
    - "hierarchical coding for distributed computing"
    - "compressed coded distributed computing"
    - "a new combinatorial design of coded distributed computing"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "on the placement delivery array design for centralized coded caching scheme"

Connected through hyperedge HE-2 (induced by source paper "timely throughput optimal coded computing over cloud networks"):
  with members:
    - "hierarchical coded computation"
    - "speeding up distributed machine learning using codes"
    - "communication aware scheduling of serial tasks for dispersed computing"
    - "lagrange coded computing optimal design for resiliency security and privacy"
    - "coded computation over heterogeneous clusters"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "coding for distributed fog computing"
    - "short dot computing large linear transforms distributedly using coded short dot products"

Connected through hyperedge HE-3 (induced by source paper "resolvable designs for speeding up distributed computing"):
  with members:
    - "leveraging coding techniques for speeding up distributed computing"
    - "compressed coded distributed computing"
    - "a new combinatorial design of coded distributed computing"
    - "communication vs distributed computation an alternative trade off curve"
    - "coded caching schemes with reduced subpacketization from linear block codes"
    - "the benefit of being flexible in distributed computation"
    - "a fundamental tradeoff between computation and communication in distributed computing"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "an exact quantized decentralized gradient descent algorithm"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "quantized decentralized consensus optimization"
    - "communication vs distributed computation an alternative trade off curve"
    - "a fundamental tradeoff between computation and communication in distributed computing"
    - "qsgd communication efficient sgd via gradient quantization and encoding"
    - "fast distributed gradient methods"
    - "distributed autonomous online learning regrets and intrinsic privacy preserving properties"

Connected through hyperedge HE-5 (induced by source paper "camr coded aggregated mapreduce"):
  with members:
    - "leveraging coding techniques for speeding up distributed computing"
    - "compressed coded distributed computing"
    - "coded caching schemes with reduced subpacketization from linear block codes"
    - "a fundamental tradeoff between computation and communication in distributed computing"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "coded elastic computing"):
  with members:
    - "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"
    - "speeding up distributed gradient descent by utilizing non persistent stragglers"
    - "hierarchical coding for distributed computing"
    - "polynomially coded regression optimal straggler mitigation via data encoding"

Connected through hyperedge HE-7 (induced by source paper "wireless mapreduce distributed computing"):
  with members:
    - "leveraging coding techniques for speeding up distributed computing"
    - "speeding up distributed machine learning using codes"
    - "federated learning strategies for improving communication efficiency"
    - "distributed computing with heterogeneous communication constraints the worst case computation load and proof by contradiction"
    - "hierarchical coding for distributed computing"
    - "compressed coded distributed computing"
    - "coded distributed computing with node cooperation substantially increases speedup factors"
    - "a new combinatorial design of coded distributed computing"

Connected through hyperedge HE-8 (induced by source paper "fundamental limits of decentralized data shuffling"):
  with members:
    - "coded distributed computing with heterogeneous function assignments"
    - "cascaded coded distributed computing on heterogeneous networks"
    - "embedded index coding"
    - "the exact rate memory tradeoff for caching with uncoded prefetching"
    - "leveraging coding techniques for speeding up distributed computing"
    - "speeding up distributed machine learning using codes"
    - "storage computation and communication a fundamental tradeoff in distributed computing"
    - "on the fundamental limits of coded data shuffling for distributed learning systems"

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)