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 sequential approximation framework for coded distributed optimization"
  Abstract: Building on the previous work of Lee et al. and Ferdinand et al. on coded computation, we propose a sequential approximation framework for solving optimization problems in a distributed manner. In a distributed computation system, latency caused by individual processors ("stragglers") usually causes a significant delay in the overall process. The proposed method is powered by a sequential computation scheme, which is designed specifically for systems with stragglers. This scheme has the desirable property that the user is guaranteed to receive useful (approximate) computation results whenever a processor finishes its subtask, even in the presence of uncertain latency. In this paper, we give a coding theorem for sequentially computing matrix-vector multiplications, and the optimality of this coding scheme is also established. As an application of the results, we demonstrate solving optimization problems using a sequential approximation approach, which accelerates the algorithm in a distributed system with stragglers.

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"
    - "oversketch approximate matrix multiplication for the cloud"

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"
    - "oversketch approximate matrix multiplication for the cloud"
    - "giant globally improved approximate newton method for distributed optimization"

Connected through hyperedge HE-3 (induced by source paper "optimal load allocation for coded distributed computation in heterogeneous clusters"):
  with members:
    - "on the optimality of d2d coded caching with uncoded cache placement and one shot delivery"
    - "on model coding for distributed inference and transmission in mobile edge computing systems"
    - "coded matrix multiplication on a group based model"
    - "hierarchical coded computation"
    - "learning a code machine learning for approximate non linear coded computation"
    - "speeding up distributed machine learning using codes"
    - "speeding up distributed gradient descent by utilizing non persistent stragglers"
    - "hierarchical coding for distributed computing"

Connected through hyperedge HE-4 (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"
    - "oversketch approximate matrix multiplication for the cloud"
    - "lagrange coded computing optimal design for resiliency security and privacy"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"

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"
    - "oversketch approximate matrix multiplication for the cloud"
    - "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 fundamental tradeoff between computation and communication in distributed computing"

Connected through hyperedge HE-6 (induced by source paper "train where the data is a case for bandwidth efficient coded training"):
  with members:
    - "slack squeeze coded computing for adaptive straggler mitigation"
    - "speeding up distributed machine learning using codes"
    - "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 fundamental tradeoff between computation and communication in distributed computing"
    - "a unified coding framework for distributed computing with straggling servers"
    - "efficient task replication for fast response times in parallel computation"

Connected through hyperedge HE-7 (induced by source paper "harnessing the power of serverless runtimes for large scale optimization"):
  with members:
    - "characterizing task completion latencies in fog computing"
    - "numpywren serverless linear algebra"
    - "serving deep learning models in a serverless platform"
    - "serverless computing current trends and open problems"
    - "hemingway modeling distributed optimization algorithms"
    - "revisiting distributed synchronous sgd"
    - "asynchronous distributed admm for large scale optimization part ii linear convergence analysis and numerical performance"
    - "asynchronous distributed admm for large scale optimization part i algorithm and convergence analysis"

Connected through hyperedge HE-8 (induced by source paper "oversketch approximate matrix multiplication for the cloud"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "numpywren serverless linear algebra"
    - "on the optimal recovery threshold of coded matrix multiplication"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "occupy the cloud distributed computing for the 99"
    - "serverless computing current trends and open problems"
    - "avoiding communication in primal and dual block coordinate descent methods"
    - "a practical guide to randomized matrix computations with matlab implementations"

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)