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: "capacity of private linear computation for coded databases"
  Abstract: We consider the problem of private linear computation (PLC) in a distributed storage system. In PLC, a user wishes to compute a linear combination of $f$ messages stored in noncolluding databases while revealing no information about the coefficients of the desired linear combination to the databases. In extension of our previous work we employ linear codes to encode the information on the databases. We show that the PLC capacity, which is the ratio of the desired linear function size and the total amount of downloaded information, matches the maximum distance separable (MDS) coded capacity of private information retrieval for a large class of linear codes that includes MDS codes. In particular, the proposed converse is valid for any number of messages and linear combinations, and the capacity expression depends on the rank of the coefficient matrix obtained from all linear combinations.

Connected through hyperedge HE-1 (induced by source paper "private computation with individual and joint privacy"):
  with members:
    - "on an equivalence between single server pir with side information and locally recoverable codes"
    - "private information retrieval with private coded side information the multi server case"
    - "single server single message online private information retrieval with side information"
    - "single server multi message individually private information retrieval with side information"
    - "the role of coded side information in single server private information retrieval"
    - "multi server private information retrieval with coded side information"
    - "achievable rate of private function retrieval from mds coded databases"
    - "multi message private information retrieval with private side information"

Connected through hyperedge HE-2 (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"
    - "private computation of systematically encoded data with colluding servers"
    - "private polynomial computation from lagrange encoding"
    - "private function retrieval"
    - "the capacity of private computation"
    - "the capacity of private information retrieval"

Connected through hyperedge HE-3 (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-4 (induced by source paper "private proximity retrieval codes"):
  with members:
    - "private information retrieval from mds coded data in distributed storage systems"
    - "private computation of systematically encoded data with colluding servers"
    - "private polynomial computation from lagrange encoding"
    - "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"
    - "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 polynomial computation from lagrange encoding"
    - "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"
    - "private polynomial computation from lagrange encoding"
    - "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 "private polynomial computation from lagrange encoding"):
  with members:
    - "codedprivateml a fast and privacy preserving framework for distributed machine learning"
    - "achievable rate of private function retrieval from mds coded databases"
    - "private information retrieval from coded storage systems with colluding byzantine and unresponsive servers"
    - "cross subspace alignment and the asymptotic capacity of x secure t private information retrieval"
    - "one shot pir refinement and lifting"
    - "lagrange coded computing optimal design for resiliency security and privacy"
    - "private information retrieval from mds coded data in distributed storage systems"
    - "private computation of systematically encoded data with colluding servers"

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)