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: "the unreasonable effectiveness of the forget gate"
  Abstract: Given the success of the gated recurrent unit, a natural question is whether all the gates of the long short-term memory (LSTM) network are necessary. Previous research has shown that the forget gate is one of the most important gates in the LSTM. Here we show that a forget-gate-only version of the LSTM with chrono-initialized biases, not only provides computational savings but outperforms the standard LSTM on multiple benchmark datasets and competes with some of the best contemporary models. Our proposed network, the JANET, achieves accuracies of 99% and 92.5% on the MNIST and pMNIST datasets, outperforming the standard LSTM which yields accuracies of 98.5% and 91%.

Connected through hyperedge HE-1 (induced by source paper "improving the gating mechanism of recurrent neural networks"):
  with members:
    - "towards non saturating recurrent units for modelling long term dependencies"
    - "transformer xl attentive language models beyond a fixed length context"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "fast parametric learning with activation memorization"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "towards binary valued gates for robust lstm training"
    - "optimizing agent behavior over long time scales by transporting value"
    - "trellis networks for sequence modeling"

Connected through hyperedge HE-2 (induced by source paper "gating revisited deep multi layer rnns that can be trained"):
  with members:
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "can recurrent neural networks warp time"
    - "multi temporal land cover classification with sequential recurrent encoders"
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"
    - "skip rnn learning to skip state updates in recurrent neural networks"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "efficient orthogonal parametrisation of recurrent neural networks using householder reflections"

Connected through hyperedge HE-3 (induced by source paper "optimizing speech recognition for the edge"):
  with members:
    - "stabilizing the lottery ticket hypothesis"
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "streaming end to end speech recognition for mobile devices"
    - "toward domain invariant speech recognition via large scale training"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "snip single shot network pruning based on connection sensitivity"
    - "rethinking the value of network pruning"
    - "simple recurrent units for highly parallelizable recurrence"

Connected through hyperedge HE-4 (induced by source paper "a dynamically controlled recurrent neural network for modeling dynamical systems"):
  with members:
    - "recurrently controlled recurrent networks"
    - "adaptive skip intervals temporal abstraction for recurrent dynamical models"
    - "neural ordinary differential equations"
    - "lstm a search space odyssey"
    - "state of the art speech recognition with sequence to sequence models"
    - "dilated recurrent neural networks"
    - "bridging the gaps between residual learning recurrent neural networks and visual cortex"
    - "recurrent batch normalization"

Connected through hyperedge HE-5 (induced by source paper "recurrent neural networks with flexible gates using kernel activation functions"):
  with members:
    - "can recurrent neural networks warp time"
    - "lstm a search space odyssey"
    - "deep gate recurrent neural network"
    - "minimal gated unit for recurrent neural networks"
    - "architectural complexity measures of recurrent neural networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "regularizing rnns by stabilizing activations"
    - "depth gated lstm"

Connected through hyperedge HE-6 (induced by source paper "a novel framework for recurrent neural networks with enhancing information processing and transmission between units"):
  with members:
    - "associative long short term memory"
    - "improved semantic representations from tree structured long short term memory networks"
    - "grid long short term memory"
    - "adam a method for stochastic optimization"
    - "translating videos to natural language using deep recurrent neural networks"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"

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)