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: "results on the redundancy of universal compression for finite length sequences"
  Abstract: In this paper, we investigate the redundancy of universal coding schemes on smooth parametric sources in the finite-length regime. We derive an upper bound on the probability of the event that a sequence of length $n$, chosen using Jeffreys' prior from the family of parametric sources with $d$ unknown parameters, is compressed with a redundancy smaller than $(1-\epsilon)\frac{d}{2}\log n$ for any $\epsilon>0$. Our results also confirm that for large enough $n$ and $d$, the average minimax redundancy provides a good estimate for the redundancy of most sources. Our result may be used to evaluate the performance of universal source coding schemes on finite-length sequences. Additionally, we precisely characterize the minimax redundancy for two--stage codes. We demonstrate that the two--stage assumption incurs a negligible redundancy especially when the number of source parameters is large. Finally, we show that the redundancy is significant in the compression of small sequences.

Connected through hyperedge HE-1 (induced by source paper "universal compression with side information from a correlated source"):
  with members:
    - "a characterization of guesswork on swiftly tilting curves"
    - "asymptotics and non asymptotics for universal fixed to variable source coding"
    - "packet level network compression realization and scaling of the network wide benefits"
    - "results on the fundamental gain of memory assisted universal source coding"

Connected through hyperedge HE-2 (induced by source paper "adversarial top k ranking"):
  with members:
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "online tensor methods for learning latent variable models"
    - "tensor decompositions for learning latent variable models"
    - "rank centrality ranking from pair wise comparisons"

Connected through hyperedge HE-3 (induced by source paper "packet level network compression realization and scaling of the network wide benefits"):
  with members:
    - "a universal parallel two pass mdl context tree compression algorithm"
    - "memory assisted universal compression of network flows"
    - "results on the fundamental gain of memory assisted universal source coding"
    - "mixing strategies in data compression"
    - "on the network wide gain of memory assisted source coding"
    - "sustaining the internet with hyperbolic mapping"

Connected through hyperedge HE-4 (induced by source paper "asymptotics and non asymptotics for universal fixed to variable source coding"):
  with members:
    - "variable length compression allowing errors"
    - "guessing under source uncertainty"

Connected through hyperedge HE-5 (induced by source paper "a universal parallel two pass mdl context tree compression algorithm"):
  with members:
    - "a parallel two pass mdl context tree algorithm for universal source coding"
    - "on lossless universal compression of distributed identical sources"

Connected through hyperedge HE-6 (induced by source paper "network compression memory assisted universal coding of sources with correlated parameters"):
  with members:
    - "on lossless universal compression of distributed identical sources"
    - "memory assisted universal compression of network flows"
    - "results on the fundamental gain of memory assisted universal source coding"
    - "memory assisted universal source coding"
    - "on the network wide gain of memory assisted source coding"
    - "femtocell networks a survey"
    - "uplink capacity and interference avoidance for two tier femtocell networks"

Connected through hyperedge HE-7 (induced by source paper "on lossless universal compression of distributed identical sources"):
  with members:
    - "memory assisted universal compression of network flows"
    - "results on the fundamental gain of memory assisted universal source coding"
    - "memory assisted universal source coding"
    - "on the network wide gain of memory assisted source coding"

Connected through hyperedge HE-8 (induced by source paper "results on the fundamental gain of memory assisted universal source coding"):
  with members:
    - "memory assisted universal compression of network flows"
    - "memory assisted universal source coding"
    - "on the network wide gain of memory assisted source 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)