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: "associative long short term memory"
  Abstract: We investigate a new method to augment recurrent neural networks with extra memory without increasing the number of network parameters. The system has an associative memory based on complex-valued vectors and is closely related to Holographic Reduced Representations and Long Short-Term Memory networks. Holographic Reduced Representations have limited capacity: as they store more information, each retrieval becomes noisier due to interference. Our system in contrast creates redundant copies of stored information, which enables retrieval with reduced noise. Experiments demonstrate faster learning on multiple memorization tasks.

Connected through hyperedge HE-1 (induced by source paper "deep quaternion features for privacy protection"):
  with members:
    - "adversarial neural network inversion via auxiliary knowledge alignment"
    - "complex valued neural networks for privacy protection"
    - "interpretable complex valued neural networks for privacy protection"
    - "quaternion convolutional neural networks"
    - "privynet a flexible framework for privacy preserving deep neural network training"
    - "exploiting unintended feature leakage in collaborative learning"
    - "gazelle a low latency framework for secure neural network inference"
    - "not just privacy improving performance of private deep learning in mobile cloud"

Connected through hyperedge HE-2 (induced by source paper "state regularized recurrent neural networks"):
  with members:
    - "context free transductions with neural stacks"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "an empirical evaluation of rule extraction from recurrent neural networks"
    - "attention is all you need"
    - "frustratingly short attention spans in neural language modeling"
    - "automatic rule extraction from long short term memory networks"

Connected through hyperedge HE-3 (induced by source paper "lightweight and efficient neural natural language processing with quaternion networks"):
  with members:
    - "quaternion collaborative filtering for recommendation"
    - "quaternion convolutional neural networks"
    - "universal transformers"
    - "quaternion knowledge graph embedding"
    - "attending to mathematical language with transformers"
    - "quaternet a quaternion based recurrent model for human motion"
    - "quaternion convolutional neural networks for end to end automatic speech recognition"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "3d rotation equivariant quaternion neural networks"):
  with members:
    - "complex valued neural networks for privacy protection"
    - "quaternion equivariant capsule networks for 3d point clouds"
    - "interpretable complex valued neural networks for privacy protection"
    - "quaternion convolutional neural networks"
    - "relation shape convolutional neural network for point cloud analysis"
    - "splatnet sparse lattice networks for point cloud processing"
    - "point2sequence learning the shape representation of 3d point clouds with an attention based sequence to sequence network"
    - "pointwise rotation invariant network with adaptive sampling and 3d spherical voxel convolution"

Connected through hyperedge HE-5 (induced by source paper "recursive sketches for modular deep learning"):
  with members:
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "regularized evolution for image classifier architecture search"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "recovery guarantees for one hidden layer neural networks"
    - "learning to reason end to end module networks for visual question answering"
    - "dynamic routing between capsules"
    - "electron proton dynamics in deep learning"
    - "zero shot task generalization with multi task deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "a review on neural turing machine"):
  with members:
    - "lstm with working memory"
    - "lie access neural turing machine"
    - "dynamic neural turing machine with soft and hard addressing schemes"
    - "deep multi task learning with shared memory"
    - "terpret a probabilistic programming language for program induction"
    - "neural random access machines"
    - "a critical review of recurrent neural networks for sequence learning"
    - "deep learning in neural networks"

Connected through hyperedge HE-7 (induced by source paper "armin towards a more efficient and light weight recurrent memory network"):
  with members:
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "towards binary valued gates for robust lstm training"
    - "sparse attentive backtracking temporal creditassignment through reminding"
    - "fast slow recurrent neural networks"
    - "memory augmented neural networks with wormhole connections"
    - "skip rnn learning to skip state updates in recurrent neural networks"
    - "scaling memory augmented neural networks with sparse reads and writes"
    - "the concrete distribution a continuous relaxation of discrete random variables"

Connected through hyperedge HE-8 (induced by source paper "implementation of optical deep neural networks using the fabry perot interferometer"):
  with members:
    - "large scale optical neural networks based on photoelectric multiplication"
    - "reinforcement learning in a large scale photonic recurrent neural network"
    - "kronecker recurrent units"
    - "gated orthogonal recurrent units on learning to forget"
    - "the numerics of gans"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "searching for activation functions"
    - "on complex valued 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.NE(Neural and Evolutionary Computing)