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: "distributed and private coded matrix computation with flexible communication load"
  Abstract: Tensor operations, such as matrix multiplication, are central to large-scale machine learning applications. For user-driven tasks these operations can be carried out on a distributed computing platform with a master server at the user side and multiple workers in the cloud operating in parallel. For distributed platforms, it has been recently shown that coding over the input data matrices can reduce the computational delay, yielding a trade-off between recovery threshold and communication load. In this paper we impose an additional security constraint on the data matrices and assume that workers can collude to eavesdrop on the content of these data matrices. Specifically, we introduce a novel class of secure codes, referred to as secure generalized PolyDot codes, that generalizes previously published non-secure versions of these codes for matrix multiplication. These codes extend the state-of-the-art by allowing a flexible trade-off between recovery threshold and communication load for a fixed maximum number of colluding workers.

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"
    - "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"
    - "an application of storage optimal matdot codes for coded matrix multiplication fast k nearest neighbors estimation"

Connected through hyperedge HE-2 (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"
    - "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"
    - "on the upload versus download cost for secure and private matrix multiplication"

Connected through hyperedge HE-3 (induced by source paper "notes on communication and computation in secure distributed matrix multiplication"):
  with members:
    - "on the capacity of secure distributed matrix multiplication"
    - "on the upload versus download cost for secure and private matrix multiplication"
    - "gasp codes for secure distributed matrix multiplication"
    - "one shot pir refinement and lifting"
    - "on the capacity of secure distributed matrix multiplication"
    - "the capacity of robust private information retrieval with colluding databases"
    - "powers of tensors and fast matrix multiplication"

Connected through hyperedge HE-4 (induced by source paper "private and secure distributed matrix multiplication with flexible communication load"):
  with members:
    - "numerically stable polynomially coded computing"
    - "private sequential function computation"
    - "random khatri rao product codes for numerically stable distributed matrix multiplication"
    - "private information retrieval with private coded side information the multi server case"
    - "random convolutional coding for robust and straggler resilient distributed matrix computation"
    - "secure coded multi party computation for massive matrix operations"
    - "on the capacity of secure distributed matrix multiplication"
    - "private secure coded computation"

Connected through hyperedge HE-5 (induced by source paper "x secure t private information retrieval from mds coded storage with byzantine and unresponsive servers"):
  with members:
    - "on the capacity of secure distributed matrix multiplication"
    - "private secure coded computation"
    - "on the upload versus download cost for secure and private matrix multiplication"
    - "private information retrieval from coded storage systems with colluding byzantine and unresponsive servers"
    - "gasp codes for secure distributed matrix multiplication"
    - "rate efficiency and straggler robustness through partition in distributed two sided secure matrix computation"
    - "cross subspace alignment and the asymptotic capacity of x secure t private information retrieval"
    - "on the capacity of secure distributed matrix multiplication"

Connected through hyperedge HE-6 (induced by source paper "on the upload versus download cost for secure and private matrix multiplication"):
  with members:
    - "private secure coded computation"
    - "achievable rate of private function retrieval from mds coded databases"
    - "gasp codes for secure distributed matrix multiplication"
    - "rate efficiency and straggler robustness through partition in distributed two sided secure matrix computation"
    - "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-7 (induced by source paper "secure coded multi party computation for massive matrix operations"):
  with members:
    - "private secure coded computation"
    - "speeding up distributed machine learning using codes"
    - "gasp codes for secure distributed matrix multiplication"
    - "oversketch approximate matrix multiplication for the cloud"
    - "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-8 (induced by source paper "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"):
  with members:
    - "codedprivateml a fast and privacy preserving framework for distributed machine learning"
    - "secure coded multi party computation for massive matrix operations"
    - "private and secure distributed matrix multiplication with flexible communication load"
    - "private secure coded computation"
    - "uplink downlink tradeoff in secure distributed matrix multiplication"
    - "on the upload versus download cost for secure and private matrix multiplication"
    - "polyshard coded sharding achieves linearly scaling efficiency and security simultaneously"
    - "speeding up distributed machine learning using 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.IT(Information Theory)