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: "polar coded distributed matrix multiplication"
  Abstract: We propose a polar coding mechanism for distributed matrix multiplication. Polar codes provably achieve channel capacity and have the advantage of low encoding and decoding complexity. These aspects of polar codes enable a scalable scheme for hundreds of compute nodes in coded computation. We analyze the polarization phenomenon in the context of run times of compute nodes and characterize polarizing matrices over real numbers. We design a sequential decoder specifically for polar codes in erasure channels with real-valued input and outputs. The proposed coded computation scheme is implemented for a serverless computing platform and numerical results are provided. Numerical results illustrate that proposed coded computation scheme achieves significant speed-ups. Finally, experiments are conducted where the performance of the proposed coded computation technique is tested in solving a least squares problem using gradient descent.

Connected through hyperedge HE-1 (induced by source paper "serverless straggler mitigation using local error correcting codes"):
  with members:
    - "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"
    - "on the optimal recovery threshold of coded matrix multiplication"

Connected through hyperedge HE-2 (induced by source paper "coded distributed computing performance limits and code designs"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "a low complexity recursive approach toward code domain noma for massive communications"
    - "rateless codes for near perfect load balancing in distributed matrix vector multiplication"
    - "almost optimal scaling of reed muller codes on bec and bsc channels"
    - "coded sparse matrix multiplication"
    - "coded computation over heterogeneous clusters"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "coding for distributed fog computing"

Connected through hyperedge HE-3 (induced by source paper "distributed black box optimization via error correcting codes"):
  with members:
    - "structured evolution with compact architectures for scalable policy optimization"
    - "speeding up distributed machine learning using codes"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "rateless codes for near perfect load balancing in distributed matrix vector multiplication"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "block diagonal and lt codes for distributed computing with straggling servers"
    - "adam a method for stochastic optimization"
    - "randomized sketches of convex programs with sharp guarantees"

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)