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: "secure coded multi party computation for massive matrix operations"
  Abstract: In this paper, we consider a secure multi-party computation problem (MPC), where the goal is to offload the computation of an arbitrary polynomial function of some massive private matrices to a network of workers. The workers are not reliable, some may collude to gain information about the input data. The system is initialized by sharing a (randomized) function of each input matrix to each server. Since the input matrices are massive, the size of each share is assumed to be at most $1/k$ fraction of the input matrix, for some $k \in \mathbb{N}$. The objective is to minimize the number of workers needed to perform the computation task correctly, such that even if an arbitrary subset of $t-1$ workers, for some $t\in \mathbb{N}$, collude, they cannot gain any information about the input matrices. We propose a sharing scheme, called polynomial sharing, and show that it admits basic operations such as adding and multiplication of matrices, and transposing a matrix. By concatenating the procedures for basic operations, we show that any polynomial function of the input matrices can be calculated, subject to the problem constraints. We show that the proposed scheme can offer order-wise gain, in terms of number of workers needed, compared to the approaches formed by concatenation of job splitting and conventional MPC approaches.

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"
    - "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"
    - "distributed and private coded matrix computation with flexible communication load"
    - "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 "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"
    - "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"
    - "coded computation over heterogeneous clusters"

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"
    - "distributed and private coded matrix computation with flexible communication load"
    - "private information retrieval with private coded side information the multi server case"
    - "random convolutional coding for robust and straggler resilient distributed matrix computation"
    - "on the capacity of secure distributed matrix multiplication"
    - "private secure coded computation"

Connected through hyperedge HE-5 (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"
    - "distributed and private coded matrix computation with flexible communication load"
    - "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)