human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "private information retrieval from mds coded data in distributed storage systems"):
  Abstract: The problem of providing privacy, in the private information retrieval (PIR) sense, to users requesting data from a distributed storage system (DSS), is considered. The DSS is coded by an    $(n,k,d)$    maximum distance separable code to store the data reliably on unreliable storage nodes. Some of these nodes can be spies which report to a third party, such as an oppressive regime, which data is being requested by the user. An information theoretic PIR scheme ensures that a user can satisfy its request while revealing no information on which data is being requested to the nodes. A user can trivially achieve PIR by downloading all the data in the DSS. However, this is not a feasible solution due to its high communication cost. We construct PIR schemes with low download communication cost. When there is    $b=1$    spy node in the DSS, in other words, no collusion between the nodes, we construct PIR schemes with download cost    $\frac {1}{1-R}$    per unit of requested data (   $R=k/n$    is the code rate), achieving the information theoretic limit for linear schemes. The proposed schemes are universal since they depend on the code rate, but not on the generator matrix of the code. Also, if    $b\leq n-\delta k$    nodes collude, with    $\delta =\lfloor {\frac {n-b}{k}}\rfloor $   , we construct linear PIR schemes with download cost    $\frac {b+\delta k}{\delta }$   .

Includes members:
  Member "the capacity of private information retrieval from byzantine and colluding databases":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")
  Member "private information retrieval from mds coded data with colluding servers settling a conjecture by freij hollanti et al":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "latent variable private information retrieval")
  Member "robust private information retrieval on coded data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "private set intersection a multi message symmetric private information retrieval perspective")
  Member "pir array codes with optimal virtual server rate":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "array codes for functional pir and batch codes")
  Member "blind interference alignment for private information retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "conditional disclosure of secrets a noise and signal alignment approach")
  Member "private information retrieval in distributed storage systems using an arbitrary linear code":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards practical private information retrieval from mds array codes")
  Member "the capacity of robust private information retrieval with colluding databases":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")
  Member "private information retrieval from coded databases with colluding servers":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "latent variable private information retrieval")

Question: Please tell me which class the source paper 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)