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: "trellis networks for sequence modeling"
  Abstract: We present trellis networks, a new architecture for sequence modeling. On the one hand, a trellis network is a temporal convolutional network with special structure, characterized by weight tying across depth and direct injection of the input into deep layers. On the other hand, we show that truncated recurrent networks are equivalent to trellis networks with special sparsity structure in their weight matrices. Thus trellis networks with general weight matrices generalize truncated recurrent networks. We leverage these connections to design high-performing trellis networks that absorb structural and algorithmic elements from both recurrent and convolutional models. Experiments demonstrate that trellis networks outperform the current state of the art methods on a variety of challenging benchmarks, including word-level language modeling and character-level language modeling tasks, and stress tests designed to evaluate long-term memory retention. The code is available at this https URL .

Connected through hyperedge HE-1 (induced by source paper "accessing higher level representations in sequential transformers with feedback memory"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "reducing transformer depth on demand with structured dropout"
    - "augmenting self attention with persistent memory"
    - "compressive transformers for long range sequence modelling"
    - "pre trained language model representations for language generation"
    - "r transformer recurrent neural network enhanced transformer"
    - "transformer xl attentive language models beyond a fixed length context"
    - "universal transformers"

Connected through hyperedge HE-2 (induced by source paper "temporal convolutional attention based network for sequence modeling"):
  with members:
    - "improving neural language modeling via adversarial training"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"
    - "conditional image generation with pixelcnn decoders"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-3 (induced by source paper "compressive transformers for long range sequence modelling"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "the curious case of neural text degeneration"
    - "dynamic evaluation of transformer language models"
    - "transformer xl attentive language models beyond a fixed length context"
    - "generating long sequences with sparse transformers"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "large memory layers with product keys"

Connected through hyperedge HE-4 (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"
    - "can recurrent neural networks warp time"

Connected through hyperedge HE-5 (induced by source paper "deep equilibrium models"):
  with members:
    - "equilibrated recurrent neural network neuronal time delayed self feedback improves accuracy and stability"
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "implicit deep learning"
    - "transformer xl attentive language models beyond a fixed length context"
    - "generating long sequences with sparse transformers"
    - "pytorch an imperative style high performance deep learning library"
    - "recurrent stacking of layers for compact neural machine translation models"
    - "relational recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "compression of recurrent neural networks for efficient language modeling"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "bayesian compression for natural language processing"
    - "online embedding compression for text classification using low rank matrix factorization"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"
    - "compressing recurrent neural network with tensor train"
    - "pointing the unknown words"

Connected through hyperedge HE-7 (induced by source paper "constructing gradient controllable recurrent neural networks using hamiltonian dynamics"):
  with members:
    - "can sgd learn recurrent neural networks with provable generalization"
    - "stable architectures for deep neural networks"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "unitary evolution recurrent neural networks"
    - "a recurrent latent variable model for sequential data"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "deep visual semantic alignments for generating image descriptions"

Connected through hyperedge HE-8 (induced by source paper "adaptive ensembling unsupervised domain adaptation for political document analysis"):
  with members:
    - "there are many consistent explanations of unlabeled data why you should average"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "attention is all you need"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "self ensembling for visual domain adaptation"
    - "temporal ensembling for semi supervised learning"
    - "improved techniques for training gans"

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.LG(Machine Learning)