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: "diversity networks neural network compression using determinantal point processes"
  Abstract: We introduce Divnet, a flexible technique for learning networks with diverse neurons. Divnet models neuronal diversity by placing a Determinantal Point Process (DPP) over neurons in a given layer. It uses this DPP to select a subset of diverse neurons and subsequently fuses the redundant neurons into the selected ones. Compared with previous approaches, Divnet offers a more principled, flexible technique for capturing neuronal diversity and thus implicitly enforcing regularization. This enables effective auto-tuning of network architecture and leads to smaller network sizes without hurting performance. Moreover, through its focus on diversity and neuron fusing, Divnet remains compatible with other procedures that seek to reduce memory footprints of networks. We present experimental results to corroborate our claims: for pruning neural networks, Divnet is seen to be notably superior to competing approaches.

Connected through hyperedge HE-1 (induced by source paper "what is the state of neural network pruning"):
  with members:
    - "jointly sparse convolutional neural networks in dual spatial winograd domains"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "benchmark analysis of representative deep neural network architectures"
    - "sprintz time series compression for the internet of things"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "efficient neural audio synthesis"
    - "network compression using correlation analysis of layer responses"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"

Connected through hyperedge HE-2 (induced by source paper "exploiting channel similarity for accelerating deep convolutional neural networks"):
  with members:
    - "discrimination aware channel pruning for deep neural networks"
    - "neural architecture optimization"
    - "darts differentiable architecture search"
    - "compressing neural networks using the variational information bottleneck"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "rethinking the value of network pruning"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-3 (induced by source paper "flexible modeling of diversity with strongly log concave distributions"):
  with members:
    - "submodular maximization beyond non negativity guarantees fast algorithms and applications"
    - "fair and diverse dpp based data summarization"
    - "log concave polynomials ii high dimensional walks and an fpras for counting bases of a matroid"
    - "gdpp learning diverse generations using determinantal point process"
    - "guess free maximization of submodular and linear sums"
    - "unbiased estimates for linear regression via volume sampling"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"
    - "kronecker determinantal point processes"

Connected through hyperedge HE-4 (induced by source paper "simultaneously learning architectures and features of deep neural networks"):
  with members:
    - "interpretable convolutional filter pruning"
    - "rethinking the value of network pruning"
    - "learning efficient convolutional networks through network slimming"
    - "data driven sparse structure selection for deep neural networks"
    - "a survey of model compression and acceleration for deep neural networks"
    - "learning structured sparsity in deep neural networks"
    - "generalized backpropagation etude de cas orthogonality"
    - "pruning filters for efficient convnets"

Connected through hyperedge HE-5 (induced by source paper "efficient and robust machine learning for real world systems"):
  with members:
    - "training and inference with integers in deep neural networks"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "the high dimensional geometry of binary neural networks"
    - "training quantized nets a deeper understanding"
    - "towards accurate binary convolutional neural network"
    - "deep learning with low precision by half wave gaussian quantization"

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)