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 polynomial computation from lagrange encoding"
  Abstract: Private computation is a generalization of private information retrieval, in which a user is able to compute a function on a distributed dataset without revealing the identity of that function to the servers. In this paper it is shown that Lagrange encoding, a powerful technique for encoding Reed-Solomon codes, enables private computation in many cases of interest. In particular, we present a scheme that enables private computation of polynomials of any degree on Lagrange encoded data, while being robust to Byzantine and straggling servers, and to servers colluding to attempt to deduce the identities of the functions to be evaluated. Moreover, incorporating ideas from the well-known Shamir secret sharing scheme allows the data itself to be concealed from the servers as well. Our results extend private computation to high degree polynomials and to data-privacy, and reveal a tight connection between private computation and coded computation.

Connected through hyperedge HE-1 (induced by source paper "on the capacity of private monomial computation"):
  with members:
    - "private polynomial computation for noncolluding coded databases"
    - "achievable rate of private function retrieval from mds coded databases"
    - "capacity of private linear computation for coded databases"
    - "private computation of systematically encoded data with colluding servers"
    - "private function retrieval"
    - "the capacity of private computation"
    - "the capacity of private information retrieval"

Connected through hyperedge HE-2 (induced by source paper "private sequential function computation"):
  with members:
    - "private inner product retrieval for distributed machine learning"
    - "on the capacity of computation broadcast"
    - "private information retrieval with private coded side information the multi server case"
    - "on the asymptotic capacity of x secure t private information retrieval with graph based replicated storage"
    - "private polynomial computation for noncolluding coded databases"
    - "on the upload versus download cost for secure and private matrix multiplication"
    - "private proximity retrieval codes"
    - "achievable rate of private function retrieval from mds coded databases"

Connected through hyperedge HE-3 (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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "private proximity retrieval codes"):
  with members:
    - "capacity of private linear computation for coded databases"
    - "private information retrieval from mds coded data in distributed storage systems"
    - "private computation of systematically encoded data with colluding servers"
    - "achieving private information retrieval capacity in distributed storage using an arbitrary linear code"
    - "the capacity of private information retrieval from byzantine and colluding databases"
    - "private function retrieval"
    - "linear symmetric private information retrieval for mds coded distributed storage with colluding servers"
    - "the capacity of private computation"

Connected through hyperedge HE-5 (induced by source paper "private polynomial computation for noncolluding coded databases"):
  with members:
    - "achievable rate of private function retrieval from mds coded databases"
    - "lifting private information retrieval from two to any number of messages"
    - "capacity of private linear computation for coded databases"
    - "lagrange coded computing optimal design for resiliency security and privacy"
    - "private information retrieval from mds coded data in distributed storage systems"
    - "the asymptotic capacity of private search"
    - "private computation of systematically encoded data with colluding servers"
    - "private function retrieval"

Connected through hyperedge HE-6 (induced by source paper "private inner product retrieval for distributed machine learning"):
  with members:
    - "private polynomial computation for noncolluding coded databases"
    - "noisy private information retrieval on separability of channel coding and information retrieval"
    - "capacity of private linear computation for coded databases"
    - "fundamental limits of cache aided private information retrieval with unknown and uncoded prefetching"
    - "the capacity of private information retrieval from byzantine and colluding databases"
    - "private function retrieval"
    - "multi message private information retrieval capacity results and near optimal schemes"
    - "the capacity of private computation"

Connected through hyperedge HE-7 (induced by source paper "gasp codes for secure distributed matrix multiplication"):
  with members:
    - "distributed and private coded matrix computation with flexible communication load"
    - "an application of storage optimal matdot codes for coded matrix multiplication fast k nearest neighbors estimation"
    - "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"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"

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)