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: "private secure coded computation"
  Abstract: We introduce a variation of coded computation that ensures data security and master's privacy against workers, which is referred to as private secure coded computation. In private secure coded computation, the master needs to compute a function of its own dataset and one of the datasets in a library exclusively shared by external workers. After recovering the desired function, the workers must not be able to know which dataset in the library was desired by the master or obtain any information about the master's own data. We propose a private secure coded computation scheme for matrix multiplication, namely private secure polynomial codes, based on private polynomial codes for private coded computation. In simulations, we show that the private secure polynomial codes achieves better computation time than private polynomial codes modified for private secure coded computation.

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"
    - "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"
    - "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"
    - "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 "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"
    - "secure coded multi party computation for massive matrix operations"
    - "on the capacity of secure distributed matrix multiplication"

Connected through hyperedge HE-4 (induced by source paper "x secure t private information retrieval from mds coded storage with byzantine and unresponsive servers"):
  with members:
    - "distributed and private coded matrix computation with flexible communication load"
    - "on the capacity of secure distributed matrix multiplication"
    - "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-5 (induced by source paper "on the upload versus download cost for secure and private matrix multiplication"):
  with members:
    - "distributed and private coded matrix computation with flexible communication load"
    - "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-6 (induced by source paper "secure coded multi party computation for massive matrix operations"):
  with members:
    - "distributed and private coded matrix computation with flexible communication load"
    - "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-7 (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"
    - "secure coded multi party computation for massive matrix operations"
    - "private and secure distributed matrix multiplication with flexible communication load"
    - "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)