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: "long short term memory as a dynamically computed element wise weighted sum"
  Abstract: LSTMs were introduced to combat vanishing gradients in simple RNNs by augmenting them with gated additive recurrent connections. We present an alternative view to explain the success of LSTMs: the gates themselves are versatile recurrent models that provide more representational power than previously appreciated. We do this by decoupling the LSTM's gates from the embedded simple RNN, producing a new class of RNNs where the recurrence computes an element-wise weighted sum of context-independent functions of the input. Ablations on a range of problems demonstrate that the gating mechanism alone performs as well as an LSTM in most settings, strongly suggesting that the gates are doing much more in practice than just alleviating vanishing gradients.

Connected through hyperedge HE-1 (induced by source paper "fundamentals of recurrent neural network rnn and long short term memory lstm network"):
  with members:
    - "latent odes for irregularly sampled time series"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "neural ordinary differential equations"
    - "recent advances in recurrent neural networks"
    - "nais net stable deep networks from non autonomous differential equations"
    - "lstm a search space odyssey"
    - "beyond finite layer neural networks bridging deep architectures and numerical differential equations"

Connected through hyperedge HE-2 (induced by source paper "univse robust visual semantic embeddings via structured semantic representations"):
  with members:
    - "finding beans in burgers deep semantic visual embedding with localization"
    - "learning visually grounded semantics from contrastive adversarial samples"
    - "image generation from scene graphs"
    - "foil it find one mismatch between image and language caption"
    - "vse improving visual semantic embeddings with hard negatives"
    - "weakly supervised visual grounding of phrases with linguistic structures"
    - "a structured self attentive sentence embedding"
    - "instance aware image and sentence matching with selective multimodal lstm"

Connected through hyperedge HE-3 (induced by source paper "hybrid neural models for sequence modelling the best of three worlds"):
  with members:
    - "seq2biseq bidirectional output wise recurrent neural networks for sequence modelling"
    - "universal transformers"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "label dependencies aware recurrent neural networks"
    - "improving recurrent neural networks for sequence labelling"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-4 (induced by source paper "visualizing rnn states with predictive semantic encodings"):
  with members:
    - "rnnbow visualizing learning via backpropagation gradients in rnns"
    - "understanding hidden memories of recurrent neural networks"
    - "why should i trust you explaining the predictions of any classifier"
    - "understanding neural networks through representation erasure"
    - "lstmvis a tool for visual analysis of hidden state dynamics in recurrent neural networks"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "visualizing and understanding neural models in nlp"
    - "visualizing and understanding recurrent networks"

Connected through hyperedge HE-5 (induced by source paper "rnns implicitly implement tensor product representations"):
  with members:
    - "assessing composition in sentence vector representations"
    - "on the evaluation of semantic phenomena in neural machine translation using natural language inference"
    - "learning distributed representations of symbolic structure using binding and unbinding operations"
    - "relational inductive biases deep learning and graph networks"
    - "learning to reason with third order tensor products"
    - "tree to tree neural networks for program translation"
    - "evaluating compositionality in sentence embeddings"
    - "senteval an evaluation toolkit for universal sentence representations"

Connected through hyperedge HE-6 (induced by source paper "on tree based neural sentence modeling"):
  with members:
    - "learning visually grounded semantics from contrastive adversarial samples"
    - "deep rnns encode soft hierarchical syntax"
    - "structured attention networks"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "discourse based objectives for fast unsupervised sentence representation learning"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "jointly learning sentence embeddings and syntax with unsupervised tree lstms"
    - "modeling source syntax for neural machine translation"

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)