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: "deepcabac context adaptive binary arithmetic coding for deep neural network compression"
  Abstract: We present DeepCABAC, a novel context-adaptive binary arithmetic coder for compressing deep neural networks. It quantizes each weight parameter by minimizing a weighted rate-distortion function, which implicitly takes the impact of quantization on to the accuracy of the network into account. Subsequently, it compresses the quantized values into a bitstream representation with minimal redundancies. We show that DeepCABAC is able to reach very high compression ratios across a wide set of different network architectures and datasets. For instance, we are able to compress by x63.6 the VGG16 ImageNet model with no loss of accuracy, thus being able to represent the entire network with merely 8.7MB.

Connected through hyperedge HE-1 (induced by source paper "scalable model compression by entropy penalized reparameterization"):
  with members:
    - "uniq uniform noise injection for the quantization of neural networks"
    - "uniq uniform noise injection for non uniform quantization of neural networks"
    - "relaxed quantization for discretized neural networks"
    - "entropy constrained training of deep neural networks"
    - "coreset based neural network compression"
    - "end to end optimized image compression"
    - "ternary weight networks"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-2 (induced by source paper "when machine learning meets wireless cellular networks deployment challenges and applications"):
  with members:
    - "human centered artificial intelligence and machine learning"
    - "machine learning in the air"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "scalable agent alignment via reward modeling a research direction"
    - "rogue drone detection a machine learning approach"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "artificial neural networks based machine learning for wireless networks a tutorial"
    - "cell free massive mimo versus small cells"

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)