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 entropy coding combining speed of huffman coding with compression rate of arithmetic coding"
  Abstract: The modern data compression is mainly based on two approaches to entropy coding: Huffman (HC) and arithmetic/range coding (AC). The former is much faster, but approximates probabilities with powers of 2, usually leading to relatively low compression rates. The latter uses nearly exact probabilities - easily approaching theoretical compression rate limit (Shannon entropy), but at cost of much larger computational cost. #R##N#Asymmetric numeral systems (ANS) is a new approach to accurate entropy coding, which allows to end this trade-off between speed and rate: the recent implementation [1] provides about $50\%$ faster decoding than HC for 256 size alphabet, with compression rate similar to provided by AC. This advantage is due to being simpler than AC: using single natural number as the state, instead of two to represent a range. Beside simplifying renormalization, it allows to put the entire behavior for given probability distribution into a relatively small table: defining entropy coding automaton. The memory cost of such table for 256 size alphabet is a few kilobytes. There is a large freedom while choosing a specific table - using pseudorandom number generator initialized with cryptographic key for this purpose allows to simultaneously encrypt the data. #R##N#This article also introduces and discusses many other variants of this new entropy coding approach, which can provide direct alternatives for standard AC, for large alphabet range coding, or for approximated quasi arithmetic coding.

Connected through hyperedge HE-1 (induced by source paper "on optimally partitioning variable byte codes"):
  with members:
    - "vectorized vbyte decoding"
    - "on optimally partitioning a text to improve its compression"
    - "improving table compression with combinatorial optimization"

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 "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"
    - "quasi succinct indices"

Connected through hyperedge HE-5 (induced by source paper "variable byte encoding is now space efficient too"):
  with members:
    - "stream vbyte faster byte oriented integer compression"
    - "universal indexes for highly repetitive document collections"
    - "vectorized vbyte decoding"
    - "quasi succinct indices"
    - "decoding billions of integers per second through vectorization"
    - "on optimally partitioning a text to improve its compression"
    - "improving table compression with combinatorial optimization"

Connected through hyperedge HE-6 (induced by source paper "sprintz time series compression for the internet of things"):
  with members:
    - "a general simd based approach to accelerating compression algorithms"
    - "compressed sensing of multi channel eeg signals the simultaneous cosparsity and low rank optimization"
    - "decoding billions of integers per second through vectorization"
    - "a better alternative to piecewise linear time series segmentation"

Connected through hyperedge HE-7 (induced by source paper "nonuniform probability modulation for reducing energy consumption of remote sensors"):
  with members:
    - "embedding grayscale halftone pictures in qr codes using correction trees"
    - "correction trees as an alternative to turbo codes and low density parity check codes"
    - "optimal encoding on discrete lattice with translational invariant constrains using statistical algorithms"

Connected through hyperedge HE-8 (induced by source paper "lightweight compression with encryption based on asymmetric numeral systems"):
  with members:
    - "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)