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: "generation of innovative and sparse encoding vectors for broadcast systems with feedback"
  Abstract: In the application of linear network coding to wireless broadcasting with feedback, we prove that the problem of determining the existence of an innovative encoding vector is NP-complete when the finite field size is two. When the finite field size is larger than or equal to the number of users, it is shown that we can always find an encoding vector which is both innovative and sparse. The sparsity can be utilized in speeding up the decoding process. An efficient algorithm to generate innovative and sparse encoding vectors is developed. Simulations show that the delay performance of our scheme with binary finite field outperforms a number of existing schemes in terms of average and worst-case delay.

Connected through hyperedge HE-1 (induced by source paper "approximating throughput and packet decoding delay in linear network coded wireless broadcast"):
  with members:
    - "performance characterization and transmission schemes for instantly decodable network coding in wireless broadcast"

Connected through hyperedge HE-2 (induced by source paper "on deterministic linear network coded broadcast and its relation to matroid theory"):
  with members:
    - "linear network coding linear index coding and representable discrete polymatroids"
    - "on throughput and decoding delay performance of instantly decodable network coding"
    - "an equivalence between network coding and index coding"
    - "round robin streaming with generations"
    - "online network coding for optimal throughput and delay the three receiver case"
    - "on the index coding problem and its relation to network coding and matroid theory"

Connected through hyperedge HE-3 (induced by source paper "optimal solution for the index coding problem using network coding over gf 2"):
  with members:
    - "effects of the generation size and overlap on throughput and complexity in randomized linear 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)