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: "learning simpler language models with the differential state framework"
  Abstract: Learning useful information across long time lags is a critical and difficult problem for temporal neural models in tasks such as language modeling. Existing architectures that address the issue are often complex and costly to train. The Differential State Framework (DSF) is a simple and high-performing design that unifies previously introduced gated neural models. DSF models maintain longer-term memory by learning to interpolate between a fast-changing data-driven representation and a slowly changing, implicitly stable state. This requires hardly any more parameters than a classical, simple recurrent network. Within the DSF framework, a new architecture is presented, the Delta-RNN. In language modeling at the word and character levels, the Delta-RNN outperforms popular complex architectures, such as the Long Short Term Memory (LSTM) and the Gated Recurrent Unit (GRU), and, when regularized, performs comparably to several state-of-the-art baselines. At the subword level, the Delta-RNN's performance is comparable to that of complex gated architectures.

Connected through hyperedge HE-1 (induced by source paper "spiking neural predictive coding for continual learning from data streams"):
  with members:
    - "a biologically plausible supervised learning method for spiking neural networks using the symmetric stdp rule"
    - "unsupervised learning by competing hidden units"
    - "lifelong neural predictive coding sparsity yields less forgetting when learning cumulatively"
    - "bio inspired digit recognition using reward modulated spike timing dependent plasticity in deep convolutional networks"
    - "like a baby visually situated neural language acquisition"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "continual learning of recurrent neural networks by locally aligning distributed representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "an empirical exploration of deep recurrent connections and memory cells using neuro evolution"):
  with members:
    - "a specialized evolutionary strategy using mean absolute error random sampling to design recurrent neural networks"
    - "investigating recurrent neural network memory structures using neuro evolution"
    - "low cost recurrent neural network expected performance evaluation"
    - "from nodes to networks evolving recurrent neural networks"
    - "evolving deep neural networks"
    - "gated orthogonal recurrent units on learning to forget"
    - "evolving deep convolutional neural networks for image classification"
    - "a genetic programming approach to designing convolutional neural network architectures"

Connected through hyperedge HE-3 (induced by source paper "investigating recurrent neural network memory structures using neuro evolution"):
  with members:
    - "accelerating the evolution of convolutional neural networks with node level mutations and epigenetic weight initialization"
    - "from nodes to networks evolving recurrent neural networks"
    - "perturbative neural networks"
    - "evolving deep neural networks"
    - "large scale evolution of image classifiers"
    - "evolving deep convolutional neural networks for image classification"
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "large scale evolution of convolutional neural networks using volunteer computing"

Connected through hyperedge HE-4 (induced by source paper "investigating rnn memory using neuro evolution investigating recurrent neural network memory structures using neuro evolution"):
  with members:
    - "from nodes to networks evolving recurrent neural networks"
    - "perturbative neural networks"
    - "learned iterative decoding for lossy image compression systems"
    - "evolving deep neural networks"
    - "large scale evolution of image classifiers"
    - "evolving deep convolutional neural networks for image classification"
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "using neural generative models to release synthetic twitter corpora with reduced stylometric identifiability of users"

Connected through hyperedge HE-5 (induced by source paper "language modeling through long term memory network"):
  with members:
    - "enhancing semantic word representations by embedding deeper word relationships"
    - "data noising as smoothing in neural network language models"
    - "recent trends in deep learning based natural language processing"
    - "learning over long time lags"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "attention based models for speech recognition"
    - "ask me anything dynamic memory networks for natural language processing"
    - "neural turing machines"

Connected through hyperedge HE-6 (induced by source paper "the ant swarm neuro evolution procedure for optimizing recurrent networks"):
  with members:
    - "a specialized evolutionary strategy using mean absolute error random sampling to design recurrent neural networks"
    - "investigating recurrent neural network memory structures using neuro evolution"
    - "low cost recurrent neural network expected performance evaluation"
    - "accelerating the evolution of convolutional neural networks with node level mutations and epigenetic weight initialization"
    - "evolving deep neural networks"
    - "large scale evolution of convolutional neural networks using volunteer computing"
    - "minimal gated unit for recurrent neural networks"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-7 (induced by source paper "continual learning of recurrent neural networks by locally aligning distributed representations"):
  with members:
    - "a neural temporal model for human motion prediction"
    - "sparse attentive backtracking long range credit assignment in recurrent networks"
    - "approximating real time recurrent learning with random kronecker factors"
    - "biologically motivated algorithms for propagating local target representations"
    - "conducting credit assignment by aligning local representations"
    - "lstm a search space odyssey"
    - "learning to adapt by minimizing discrepancy"
    - "unbiased online recurrent optimization"

Connected through hyperedge HE-8 (induced by source paper "the unreasonable effectiveness of the forget gate"):
  with members:
    - "can recurrent neural networks warp time"
    - "lstm a search space odyssey"
    - "dilated recurrent neural networks"
    - "phased lstm accelerating recurrent network training for long or event based sequences"
    - "minimal gated unit for recurrent neural networks"
    - "wavenet a generative model for raw audio"
    - "architectural complexity measures of recurrent neural networks"
    - "zoneout regularizing rnns by randomly preserving hidden activations"

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)