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: "block sparse recurrent neural networks"
  Abstract: Recurrent Neural Networks (RNNs) are used in state-of-the-art models in domains such as speech recognition, machine translation, and language modelling. Sparsity is a technique to reduce compute and memory requirements of deep learning models. Sparse RNNs are easier to deploy on devices and high-end server processors. Even though sparse operations need less compute and memory relative to their dense counterparts, the speed-up observed by using sparse operations is less than expected on different hardware platforms. In order to address this issue, we investigate two different approaches to induce block sparsity in RNNs: pruning blocks of weights in a layer and using group lasso regularization with pruning to create blocks of weights with zeros. Using these techniques, we can create block-sparse RNNs with sparsity ranging from 80% to 90% with a small loss in accuracy. This technique allows us to reduce the model size by roughly 10x. Additionally, we can prune a larger dense network to recover this loss in accuracy while maintaining high block sparsity and reducing the overall parameter count. Our technique works with a variety of block sizes up to 32x32. Block-sparse RNNs eliminate overheads related to data storage and irregular memory accesses while increasing hardware efficiency compared to unstructured sparsity.

Connected through hyperedge HE-1 (induced by source paper "block wise dynamic sparseness"):
  with members:
    - "you look twice gaternet for dynamic filter selection in cnns"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "learning efficient convolutional networks through network slimming"
    - "exploring sparsity in recurrent neural networks"
    - "learning structured sparsity in deep neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "language modeling with gated convolutional networks"
    - "eie efficient inference engine on compressed deep neural network"

Connected through hyperedge HE-2 (induced by source paper "recurrent neural networks an embedded computing perspective"):
  with members:
    - "deep neural network approximation for custom hardware where we ve been where we re going"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "single stream parallelization of recurrent neural networks for low power and fast inference"
    - "effective quantization approaches for recurrent neural networks"
    - "alternating multi bit quantization for recurrent neural networks"
    - "a survey on methods and theories of quantized neural networks"
    - "finn l library extensions and design trade off analysis for variable precision lstm networks on fpgas"
    - "tensor decomposition for compressing recurrent neural network"

Connected through hyperedge HE-3 (induced by source paper "fully quantized transformer for improved translation"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "efficient 8 bit quantization of transformer neural machine language translation model"
    - "weighted transformer network for machine translation"
    - "model compression via distillation and quantization"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "scaling neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "groupreduce block wise low rank approximation for neural language model shrinking"

Connected through hyperedge HE-4 (induced by source paper "full stack optimization for accelerating cnns with fpga validation"):
  with members:
    - "packing sparse convolutional neural networks for efficient systolic array implementations column combining under joint optimization"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "from hashing to cnns training binaryweight networks via hashing"
    - "design flow of accelerating hybrid extremely low bit width neural network in embedded fpga"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "pact parameterized clipping activation for quantized neural networks"
    - "in datacenter performance analysis of a tensor processing unit"
    - "shift a zero flop zero parameter alternative to spatial convolutions"

Connected through hyperedge HE-5 (induced by source paper "structured pruning of recurrent neural networks through neuron selection"):
  with members:
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "discrimination aware channel pruning for deep neural networks"
    - "compressing recurrent neural networks with tensor ring for action recognition"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "learning intrinsic sparse structures within long short term memory"
    - "regularizing and optimizing lstm language models"
    - "exploring sparsity in recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "compressing rnns for iot devices by 15 38x using kronecker products"):
  with members:
    - "tucker tensor layer in fully connected neural networks"
    - "run time efficient rnn compression for inference on edge devices"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "measuring scheduling efficiency of rnns for nlp applications"
    - "the state of sparsity in deep neural networks"
    - "structured weight matrices based hardware accelerators in deep neural networks fpgas and asics"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"

Connected through hyperedge HE-7 (induced by source paper "one shot pruning of recurrent neural networks by jacobian spectrum evaluation"):
  with members:
    - "dynamical isometry and a mean field theory of lstms and grus"
    - "the state of sparsity in deep neural networks"
    - "trace norm regularization and faster inference for embedded speech recognition rnns"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "snip single shot network pruning based on connection sensitivity"
    - "grow and prune compact fast and accurate lstms"
    - "groupreduce block wise low rank approximation for neural language model shrinking"

Connected through hyperedge HE-8 (induced by source paper "cross channel intragroup sparsity neural network"):
  with members:
    - "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "dnn dataflow choice is overrated"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "channelnets compact and efficient convolutional neural networks via channel wise convolutions"
    - "soft filter pruning for accelerating deep convolutional neural networks"

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)