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: "network throughput optimization via error correcting codes"
  Abstract: A new network construction method is presented for building of scalable, high throughput, low latency networks. The method is based on the exact equivalence discovered between the problem of maximizing network throughput (measured as bisection bandwidth) for a large class of practically interesting Cayley graphs and the problem of maximizing codeword distance for linear error correcting codes. Since the latter problem belongs to a more mature research field with large collections of optimal solutions available, a simple translation recipe is provided for converting the existent optimal error correcting codes into optimal throughput networks. The resulting networks, called here Long Hop networks, require 1.5-5 times fewer switches, 2-6 times fewer internal cables and 1.2-2 times fewer `average hops' than the best presently known networks for the same number of ports provided and the same total throughput. These advantage ratios increase with the network size and switch radix. Independently interesting byproduct of the discovered equivalence is an efficient O(n*log(n)) algorithm based on Walsh-Hadamard transform for computing exact bisections of this class of Cayley graphs (this is NP complete problem for general graphs).

Connected through hyperedge HE-1 (induced by source paper "slim fly a cost effective low diameter network topology"):
  with members:
    - "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"
    - "graph processing on fpgas taxonomy survey challenges"
    - "red blue pebbling revisited near optimal parallel matrix matrix multiplication"
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "network accelerated non contiguous memory transfers"
    - "communication efficient jaccard similarity for high performance distributed genome comparisons"
    - "slim graph practical lossy graph compression for approximate graph processing storage and analytics"
    - "survey and taxonomy of lossless graph compression and space efficient graph representations"

Connected through hyperedge HE-2 (induced by source paper "optimal networks from error correcting codes"):
  with members:
    - "jellyfish networking data centers randomly"

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)