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: "nested lstms"
  Abstract: We propose Nested LSTMs (NLSTM), a novel RNN architecture with multiple levels of memory. Nested LSTMs add depth to LSTMs via nesting as opposed to stacking. The value of a memory cell in an NLSTM is computed by an LSTM cell, which has its own inner memory cell. Specifically, instead of computing the value of the (outer) memory cell as $c^{outer}_t = f_t \odot c_{t-1} + i_t \odot g_t$, NLSTM memory cells use the concatenation $(f_t \odot c_{t-1}, i_t \odot g_t)$ as input to an inner LSTM (or NLSTM) memory cell, and set $c^{outer}_t$ = $h^{inner}_t$. Nested LSTMs outperform both stacked and single-layer LSTMs with similar numbers of parameters in our experiments on various character-level language modeling tasks, and the inner memories of an LSTM learn longer term dependencies compared with the higher-level units of a stacked LSTM.

Connected through hyperedge HE-1 (induced by source paper "a memory enhanced lstm for modeling complex temporal dependencies"):
  with members:
    - "long short term memory networks for machine reading"
    - "recurrent highway networks"
    - "recurrent memory networks for language modeling"
    - "hierarchical multiscale recurrent neural networks"
    - "assessing the ability of lstms to learn syntax sensitive dependencies"
    - "a large annotated corpus for learning natural language inference"
    - "highway long short term memory rnns for distant speech recognition"
    - "how to construct deep recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "long short term attention"):
  with members:
    - "structured attention networks"
    - "lstm a search space odyssey"
    - "attention is all you need"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "a structured self attentive sentence embedding"
    - "a deep reinforced model for abstractive summarization"
    - "phased lstm accelerating recurrent network training for long or event based sequences"
    - "learning structured sparsity in deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "the emergent algebraic structure of rnns and embeddings in nlp"):
  with members:
    - "learning to compute word embeddings on the fly"
    - "monotonic chunkwise attention"
    - "starspace embed all the things"
    - "gated orthogonal recurrent units on learning to forget"
    - "attention is all you need"
    - "attention over attention neural networks for reading comprehension"
    - "advances in pre training distributed word representations"
    - "comparative study of cnn and rnn for natural language processing"

Connected through hyperedge HE-4 (induced by source paper "generating albums with samplernn to imitate metal rock and punk bands"):
  with members:
    - "efficient neural audio synthesis"
    - "generating black metal and math rock beyond bach beethoven and beatles"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "generating nontrivial melodies for music as a service"
    - "progressive growing of gans for improved quality stability and variation"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "deep voice real time neural text to speech"
    - "dilated recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "forecasting transportation network speed using deep capsule networks with nested lstm models"):
  with members:
    - "deep bidirectional and unidirectional lstm recurrent neural network for network wide traffic speed prediction"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks"
    - "dynamic routing between capsules"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"

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.CL(Computation and Language)