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: "polynomial codes an optimal design for high dimensional coded matrix multiplication"
  Abstract: We consider a large-scale matrix multiplication problem where the computation is carried out using a distributed system with a master node and multiple worker nodes, where each worker can store parts of the input matrices. We propose a computation strategy that leverages ideas from coding theory to design intermediate computations at the worker nodes, in order to efficiently deal with straggling workers. The proposed strategy, named as \emph{polynomial codes}, achieves the optimum recovery threshold, defined as the minimum number of workers that the master needs to wait for in order to compute the output. Furthermore, by leveraging the algebraic structure of polynomial codes, we can map the reconstruction problem of the final output to a polynomial interpolation problem, which can be solved efficiently. Polynomial codes provide order-wise improvement over the state of the art in terms of recovery threshold, and are also optimal in terms of several other metrics. Furthermore, we extend this code to distributed convolution and show its order-wise optimality.

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 "entangled polynomial codes for secure private and batch distributed matrix multiplication breaking the cubic barrier"):
  with members:
    - "codedprivateml a fast and privacy preserving framework for distributed machine learning"
    - "distributed and private coded matrix computation with flexible communication load"
    - "secure coded multi party computation for massive matrix operations"
    - "on the capacity of secure distributed matrix multiplication"
    - "private and secure distributed matrix multiplication with flexible communication load"
    - "private secure coded computation"
    - "harmonic coding an optimal linear code for privacy preserving gradient type computation"
    - "uplink downlink tradeoff in secure distributed matrix multiplication"

Connected through hyperedge HE-4 (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-5 (induced by source paper "straggler resistant distributed matrix computation via coding theory"):
  with members:
    - "numerically stable polynomially coded computing"
    - "distributed matrix vector multiplication a convolutional coding approach"
    - "random khatri rao product codes for numerically stable distributed matrix multiplication"
    - "random convolutional coding for robust and straggler resilient distributed matrix computation"
    - "numerically stable coded matrix computations via circulant and rotation matrix embeddings"
    - "universally decodable matrices for distributed matrix vector multiplication"
    - "speeding up distributed machine learning using codes"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"

Connected through hyperedge HE-6 (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-7 (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"
    - "coded computing for distributed graph analytics"
    - "hierarchical coding for distributed computing"
    - "compressed coded distributed computing"
    - "a new combinatorial design of coded distributed computing"
    - "on the placement delivery array design for centralized coded caching scheme"

Connected through hyperedge HE-8 (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"

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)