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: "oversketch approximate matrix multiplication for the cloud"
  Abstract: We propose OverSketch, an approximate algorithm for distributed matrix multiplication in serverless computing. OverSketch leverages ideas from matrix sketching and high-performance computing to enable cost-efficient multiplication that is resilient to faults and straggling nodes pervasive in low-cost serverless architectures. We establish statistical guarantees on the accuracy of OverSketch and empirically validate our results by solving a large-scale linear program using interior-point methods and demonstrate a 34% reduction in compute time on AWS Lambda.

Connected through hyperedge HE-1 (induced by source paper "serverless straggler mitigation using local error correcting codes"):
  with members:
    - "polar coded distributed matrix multiplication"
    - "oversketched newton fast convex optimization for serverless systems"
    - "cloud programming simplified a berkeley view on serverless computing"
    - "collaborative decoding of polynomial codes for distributed computation"
    - "serverless computing one step forward two steps back"
    - "learning a code machine learning for approximate non linear coded computation"
    - "speeding up distributed machine learning using codes"
    - "on the optimal recovery threshold of coded matrix multiplication"

Connected through hyperedge HE-2 (induced by source paper "oversketched newton fast convex optimization for serverless systems"):
  with members:
    - "cloud programming simplified a berkeley view on serverless computing"
    - "harnessing the power of serverless runtimes for large scale optimization"
    - "serverless computing one step forward two steps back"
    - "a distributed second order algorithm you can trust"
    - "speeding up distributed machine learning using codes"
    - "numpywren serverless linear algebra"
    - "giant globally improved approximate newton method for distributed optimization"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"

Connected through hyperedge HE-3 (induced by source paper "slack squeeze coded computing for adaptive straggler mitigation"):
  with members:
    - "codedprivateml a fast and privacy preserving framework for distributed machine learning"
    - "parity models a general framework for coding based resilience in ml inference"
    - "hop heterogeneity aware decentralized training"
    - "collage inference tolerating stragglers in distributed neural network inference using coding"
    - "speeding up distributed machine learning using codes"
    - "lagrange coded computing optimal design for resiliency security and privacy"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "coding method for parallel iterative linear solver"

Connected through hyperedge HE-4 (induced by source paper "secure coded multi party computation for massive matrix operations"):
  with members:
    - "distributed and private coded matrix computation with flexible communication load"
    - "private secure coded computation"
    - "speeding up distributed machine learning using codes"
    - "gasp codes for secure distributed matrix multiplication"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "on the capacity of secure distributed matrix multiplication"
    - "on the optimal recovery threshold of coded matrix multiplication"
    - "lagrange coded computing optimal design for resiliency security and privacy"

Connected through hyperedge HE-5 (induced by source paper "distributed matrix multiplication using speed adaptive coding"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "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 method for parallel iterative linear solver"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "a sequential approximation framework for coded distributed optimization"
    - "a fundamental tradeoff between computation and communication in distributed computing"

Connected through hyperedge HE-6 (induced by source paper "gradient coding based on block designs for mitigating adversarial stragglers"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "near optimal straggler mitigation for distributed gradient methods"
    - "occupy the cloud distributed computing for the 99"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "gradient coding from cyclic mds codes and expander graphs"
    - "revisiting distributed synchronous sgd"
    - "effective straggler mitigation which clones should attack and when"

Connected through hyperedge HE-7 (induced by source paper "straggler resilient serverless computing based on polar codes"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "on the optimal recovery threshold of coded matrix multiplication"
    - "rateless codes for near perfect load balancing in distributed matrix vector multiplication"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "occupy the cloud distributed computing for the 99"
    - "block diagonal and lt codes for distributed computing with straggling servers"
    - "fnt based reed solomon erasure codes"

Connected through hyperedge HE-8 (induced by source paper "polar coded distributed matrix multiplication"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "on the optimal recovery threshold of coded matrix multiplication"
    - "rateless codes for near perfect load balancing in distributed matrix vector multiplication"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "occupy the cloud distributed computing for the 99"
    - "block diagonal and lt codes for distributed computing with straggling servers"
    - "fnt based reed solomon erasure codes"

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)