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: "interleaved entropy coders"
  Abstract: The ANS family of arithmetic coders developed by Jarek Duda has the unique property that encoder and decoder are completely symmetric in the sense that a decoder reading bits will be in the exact same state that the encoder was in when writing those bits---all "buffering" of information is explicitly part of the coder state and identical between encoder and decoder. As a consequence, the output from multiple ABS/ANS coders can be interleaved into the same bitstream without any additional metadata. This allows for very efficient encoding and decoding on CPUs supporting superscalar execution or SIMD instructions, as well as GPU implementations. We also show how interleaving without additional metadata can be implemented for any entropy coder, at some increase in encoder complexity.

Connected through hyperedge HE-1 (induced by source paper "a tutorial on the range variant of asymmetric numeral systems"):
  with members:
    - "practical lossless compression with latent variables using bits back coding"
    - "lightweight compression with encryption based on asymmetric numeral systems"
    - "asymmetric numeral systems"

Connected through hyperedge HE-2 (induced by source paper "compression with flows via local bits back coding"):
  with members:
    - "practical lossless compression with latent variables using bits back coding"
    - "videoflow a flow based generative model for video"
    - "generating long sequences with sparse transformers"
    - "bit swap recursive bits back coding for lossless compression with hierarchical latent variables"
    - "waveglow a flow based generative network for speech synthesis"
    - "invertible residual networks"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "ffjord free form continuous dynamics for scalable reversible generative models"

Connected through hyperedge HE-3 (induced by source paper "practical lossless compression with latent variables using bits back coding"):
  with members:
    - "joint autoregressive and hierarchical priors for learned image compression"
    - "deep generative models for distribution preserving lossy compression"
    - "end to end optimized image compression"
    - "pixelvae a latent variable model for natural images"
    - "pixel recurrent neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "asymmetric numeral systems"

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)