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: "a machine learning perspective on predictive coding with paq"
  Abstract: PAQ8 is an open source lossless data compression algorithm that currently achieves the best compression rates on many benchmarks. This report presents a detailed description of PAQ8 from a statistical machine learning perspective. It shows that it is possible to understand some of the modules of PAQ8 and use this understanding to improve the method. However, intuitive statistical explanations of the behavior of other modules remain elusive. We hope the description in this report will be a starting point for discussions that will increase our understanding, lead to improvements to PAQ8, and facilitate a transfer of knowledge from PAQ8 to other machine learning methods, such a recurrent neural networks and stochastic memoizers. Finally, the report presents a broad range of new applications of PAQ to machine learning tasks including language modeling and adaptive text prediction, adaptive game playing, classification, and compression using features from the field of deep learning.

Connected through hyperedge HE-1 (induced by source paper "a neural network prefetcher for arbitrary memory access patterns"):
  with members:
    - "learning memory access patterns"
    - "in datacenter performance analysis of a tensor processing unit"
    - "lstm a search space odyssey"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "bitwise neural networks"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "ternary neural networks for resource efficient ai applications"
    - "quantized neural networks training neural networks with low precision weights and activations"

Connected through hyperedge HE-2 (induced by source paper "gated linear networks"):
  with members:
    - "online learning with gated linear networks"
    - "pixel recurrent neural networks"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"
    - "linear and geometric mixtures analysis"

Connected through hyperedge HE-3 (induced by source paper "generating sequences with recurrent neural networks"):
  with members:
    - "speech recognition with deep recurrent neural networks"
    - "sequence transduction with recurrent neural networks"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"
    - "a fast and simple algorithm for training neural probabilistic language models"

Connected through hyperedge HE-4 (induced by source paper "recklessly approximate sparse coding"):
  with members:
    - "building high level features using large scale unsupervised learning"
    - "non negative sparse 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.LG(Machine Learning)