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: "sparse neural networks with large learning diversity"
  Abstract: Coded recurrent neural networks with three levels of sparsity are introduced. The first level is related to the size of messages, much smaller than the number of available neurons. The second one is provided by a particular coding rule, acting as a local constraint in the neural activity. The third one is a characteristic of the low final connection density of the network after the learning phase. Though the proposed network is very simple since it is based on binary neurons and binary connections, it is able to learn a large number of messages and recall them, even in presence of strong erasures. The performance of the network is assessed as a classifier and as an associative memory.

Connected through hyperedge HE-1 (induced by source paper "transfer learning with sparse associative memories"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "rethinking atrous convolution for semantic image segmentation"
    - "mask r cnn"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

Connected through hyperedge HE-2 (induced by source paper "sparse associative memory based on contextual code learning for disambiguating word senses"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "near lossless binarization of word embeddings"
    - "compressing word embeddings via deep compositional code learning"
    - "sparse overcomplete word vector representations"
    - "winner take all autoencoders"
    - "one billion word benchmark for measuring progress in statistical language modeling"

Connected through hyperedge HE-3 (induced by source paper "neural distributed autoassociative memories a survey"):
  with members:
    - "autoscaling bloom filter controlling trade off between true and false positives"
    - "dense associative memory is robust to adversarial inputs"
    - "fast similarity sketching"
    - "optimal las vegas locality sensitive data structures"
    - "associative memories to accelerate approximate nearest neighbor search"
    - "dense associative memory for pattern recognition"
    - "set similarity search beyond minhash"
    - "distance sensitive bloom filters without false negatives"

Connected through hyperedge HE-4 (induced by source paper "combating corrupt messages in sparse clustered associative memories"):
  with members:
    - "a low power content addressable memory based on clustered sparse networks"
    - "a massively parallel associative memory based on sparse neural networks"
    - "a study of retrieval algorithms of sparse messages in networks of neural cliques"
    - "improving sparse associative memories by escaping from bogus fixed points"

Connected through hyperedge HE-5 (induced by source paper "noise facilitation in associative memories of exponential capacity"):
  with members:
    - "performance of ldpc codes under faulty iterative decoding"
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"

Connected through hyperedge HE-6 (induced by source paper "storing sequences in binary tournament based neural networks"):
  with members:
    - "a study of retrieval algorithms of sparse messages in networks of neural cliques"

Connected through hyperedge HE-7 (induced by source paper "associative memories based on multiple valued sparse clustered networks"):
  with members:
    - "selective decoding in associative memories based on sparse clustered networks"

Connected through hyperedge HE-8 (induced by source paper "coupled neural associative memories"):
  with members:
    - "a simple proof of threshold saturation for coupled scalar recursions"
    - "approaching capacity at high rates with iterative hard decision decoding"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"

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)