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: "locally repairable convolutional codes with sliding window repair"
  Abstract: Locally repairable convolutional codes (LRCCs) for distributed storage systems (DSSs) are introduced in this work. They enable local repair, for a single node erasure, and sliding-window global repair, which can correct up to $ {\rm d}^c_j - 1 $ node erasures in a window of $ j+1 $ consecutive blocks of $ n $ nodes, where $ {\rm d}^c_j $ is the $ j $th column distance of the code. The parameter $ j $ can be adjusted, for a fixed LRCC, according to different catastrophic erasure patterns, requiring only to contact $ n(j+1) - {\rm d}^c_j + 1 $ nodes, plus less than $ \mu n $ other nodes, in the storage system, where $ \mu $ is the memory of the code. A Singleton-type bound is provided for $ {\rm d}^c_j $. If it attains such a bound, an LRCC can correct the same number of catastrophic erasures in a window of length $ n(j+1) $ as an optimal locally repairable block code of the same rate and locality, and with block length $ n(j+1) $, but being able to perform the flexible and somehow local sliding-window repair by adjusting $ j $. Furthermore, by sliding the window to consider previous or consequent nodes without erasures, or by increasing the window size, the LRCC can potentially correct more erasures in the original window of $ n(j+1) $ nodes than the optimal locally repairable block code. Finally, the concept of partial maximum distance profile (partial MDP) codes is introduced. Partial MDP codes can correct all information-theoretically correctable erasure patterns for a given locality, local distance and information rate. An explicit construction of partial MDP codes whose column distances attain the provided Singleton-type bound, up to certain parameter $ j=L $, is obtained based on known maximum sum-rank distance convolutional 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)