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 information retrieval from locally repairable databases with colluding servers"
  Abstract: Information-theoretical private information retrieval (PIR) is considered from a coded database with colluding servers. The storage code is a locally repairable code (LRC) with maximal recoverability (MR), and in particular, with optimal global minimum distance, for arbitrary code parameters: Number of local groups $ g $, locality $ r $, local distance $ \delta $, dimension $ k \leq gr $ and length $ n = g(r + \delta - 1) $. Servers are identified bijectively with local groups, and only locally non-redundant information is considered and downloaded from each server, that is, only $ r $ nodes (out of $ r+\delta-1 $) are considered per server. When the remaining MDS code, after removing all locally redundant nodes, is a linearized Reed-Solomon code, a PIR scheme is provided achieving the (download) rate $ R = (N - k - rt + 1)/N $, where $ N = gr = n - g(\delta - 1) $ is the length of the restricted MDS code, for any $ t $ colluding servers such that $ k + rt \leq N $. The field size is roughly $ g^r $, polynomial in the number of servers $ g $. Assume an arbitrarily large number of stored files. If $ N - k - rt = 0 $, the rate $ R = 1/N $ is the highest known and coincides with that of previous PIR schemes that work for any MDS storage code. If $ N - k - rt > 0 $, the achieved rate $ R > 1/N $ coincides with the best known rate of PIR schemes for MDS storage codes (but which do not work for LRCs or linearized Reed-Solomon storage codes) and is always strictly higher than that of known PIR schemes that work for arbitrary MDS storage codes. Finally, the obtained PIR scheme can also be adapted to the case where communication between the user and each server is through a linearly-coded network, achieving the same rate as previous known PIR schemes but with polynomial field sizes in the number of servers, instead of exponential.

Connected through hyperedge HE-1 (induced by source paper "robust low delay streaming pir using convolutional codes"):
  with members:
    - "private information retrieval from coded storage systems with colluding byzantine and unresponsive servers"
    - "robust private information retrieval on coded data"
    - "layered constructions for low delay streaming codes"
    - "superregular matrices and applications to convolutional codes"
    - "private information retrieval from coded databases with colluding servers"
    - "the capacity of private information retrieval from coded databases"
    - "robust streaming erasure codes based on deterministic channel approximations"
    - "decoding of convolutional codes over the erasure channel"

Connected through hyperedge HE-2 (induced by source paper "theory of supports for linear codes endowed with the sum rank metric"):
  with members:
    - "on defining generalized rank weights"
    - "skew and linearized reed solomon codes and maximum sum rank distance codes over any division ring"
    - "new criteria for mrd and gabidulin codes and some rank metric code constructions"
    - "relative generalized matrix weights of matrix codes for universal security on wire tap networks"
    - "private information retrieval from coded databases with colluding servers"
    - "rank error correcting pairs"
    - "relative generalized rank weight of linear codes and its applications to network coding"
    - "convolutional codes in rank metric with application to random network coding"

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)