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: "asymmetric numeral systems"
  Abstract: In this paper will be presented new approach to entropy coding: family of generalizations of standard numeral systems which are optimal for encoding sequence of equiprobable symbols, into asymmetric numeral systems - optimal for freely chosen probability distributions of symbols. It has some similarities to Range Coding but instead of encoding symbol in choosing a range, we spread these ranges uniformly over the whole interval. This leads to simpler encoder - instead of using two states to define range, we need only one. This approach is very universal - we can obtain from extremely precise encoding (ABS) to extremely fast with possibility to additionally encrypt the data (ANS). This encryption uses the key to initialize random number generator, which is used to calculate the coding tables. Such preinitialized encryption has additional advantage: is resistant to brute force attack - to check a key we have to make whole initialization. There will be also presented application for new approach to error correction: after an error in each step we have chosen probability to observe that something was wrong. There will be also presented application for new approach to error correction: after an error in each step we have chosen probability to observe that something was wrong. We can get near Shannon's limit for any noise level this way with expected linear time of correction.

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"
    - "interleaved entropy coders"

Connected through hyperedge HE-2 (induced by source paper "bit swap recursive bits back coding for lossless compression with hierarchical latent variables"):
  with members:
    - "practical lossless compression with latent variables using bits back coding"
    - "image transformer"
    - "efficient neural audio synthesis"
    - "progressive growing of gans for improved quality stability and variation"
    - "attention is all you need"
    - "learning hierarchical features from generative models"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "pixelsnail an improved autoregressive generative model"

Connected through hyperedge HE-3 (induced by source paper "integer discrete flows and lossless compression"):
  with members:
    - "discrete flows invertible generative models of discrete data"
    - "practical lossless compression with latent variables using bits back coding"
    - "videoflow a flow based generative model for video"
    - "emerging convolutions for generative normalizing flows"
    - "bit swap recursive bits back coding for lossless compression with hierarchical latent variables"
    - "waveglow a flow based generative network for speech synthesis"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"

Connected through hyperedge HE-4 (induced by source paper "techniques for inverted index compression"):
  with members:
    - "on optimally partitioning variable byte codes"
    - "on slicing sorted integer sequences"
    - "stream vbyte faster byte oriented integer compression"
    - "roaring bitmaps implementation of an optimized software library"
    - "compressing graphs and indexes with recursive graph bisection"
    - "consistently faster and smaller compressed bitmaps with roaring"
    - "vectorized vbyte decoding"
    - "asymmetric numeral systems entropy coding combining speed of huffman coding with compression rate of arithmetic coding"

Connected through hyperedge HE-5 (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"
    - "interleaved entropy coders"

Connected through hyperedge HE-6 (induced by source paper "lightweight compression with encryption based on asymmetric numeral systems"):
  with members:
    - "asymmetric numeral systems entropy coding combining speed of huffman coding with compression rate of arithmetic coding"

Connected through hyperedge HE-7 (induced by source paper "asymmetric numeral systems entropy coding combining speed of huffman coding with compression rate of arithmetic coding"):
  with members:
    - "optimal encoding on discrete lattice with translational invariant constrains using statistical algorithms"

Connected through hyperedge HE-8 (induced by source paper "embedding grayscale halftone pictures in qr codes using correction trees"):
  with members:
    - "correction trees as an alternative to turbo codes and low density parity check codes"

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)