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: "lossy compression in near linear time via efficient random codebooks and databases"
  Abstract: The compression-complexity trade-off of lossy compression algorithms that are based on a ran- dom codebook or a random database is examined. Motivated, in part, by recent results of Gupta- Verdu-Weissman (GVW) and their underlying connections with the pattern-matching scheme of Kontoyiannis' lossy Lempel-Ziv algorithm, we introduce a non-universal version of the lossy Lempel- Ziv method (termed LLZ). The optimality of LLZ for memoryless sources is established, and its performance is compared to that of the GVW divide-and-conquer approach. Experimental results indicate that the GVW approach often yields better compression than LLZ, but at the price of much higher memory requirements. To combine the advantages of both, we introduce a hybrid algorithm (HYB) that utilizes both the divide-and-conquer idea of GVW and the single-database structure of LLZ. It is proved that HYB shares with GVW the exact same rate-distortion perfor- mance and implementation complexity, while, like LLZ, requiring less memory, by a factor which may become unbounded, depending on the choice or the relevant design parameters. Experimental results are also presented, illustrating the performance of all three methods on data generated by simple discrete memoryless sources. In particular, the HYB algorithm is shown to outperform existing schemes for the compression of some simple discrete sources with respect to the Hamming distortion criterion.

Connected through hyperedge HE-1 (induced by source paper "an mcmc approach to universal lossy compression of analog sources"):
  with members:
    - "on the rate distortion function of bernoulli gaussian sequences"
    - "performance of polar codes for channel and source coding"
    - "rate distortion via markov chain monte carlo"

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)