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: "sparse sequence to sequence models"
  Abstract: Sequence-to-sequence models are a powerful workhorse of NLP. Most variants employ a softmax transformation in both their attention mechanism and output layer, leading to dense alignments and strictly positive output probabilities. This density is wasteful, making models less interpretable and assigning probability mass to many implausible outputs. In this paper, we propose sparse sequence-to-sequence models, rooted in a new family of $\alpha$-entmax transformations, which includes softmax and sparsemax as particular cases, and is sparse for any $\alpha > 1$. We provide fast algorithms to evaluate these transformations and their gradients, which scale well for large vocabulary sizes. Our models are able to produce sparse alignments and to assign nonzero probability to a short list of plausible outputs, sometimes rendering beam search exact. Experiments on morphological inflection and machine translation reveal consistent gains over dense models.

Connected through hyperedge HE-1 (induced by source paper "power bert accelerating bert inference for classification tasks"):
  with members:
    - "are sixteen heads really better than one"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "adaptively sparse transformers"
    - "q bert hessian based ultra low precision quantization of bert"
    - "explicit sparse transformer concentrated attention through explicit selection"
    - "albert a lite bert for self supervised learning of language representations"
    - "pruning a bert based question answering model"

Connected through hyperedge HE-2 (induced by source paper "lp sparsemap differentiable relaxed optimization for sparse structured prediction"):
  with members:
    - "learning latent trees with stochastic perturbations and differentiable dynamic programming"
    - "differentiable convex optimization layers"
    - "meta learning with implicit gradients"
    - "listops a diagnostic dataset for latent tree learning"
    - "backpropagating through structured argmax using a spigot"
    - "differentiable perturb and parse semi supervised parsing with a structured variational autoencoder"
    - "structured attention networks"
    - "learning structured text representations"

Connected through hyperedge HE-3 (induced by source paper "deep differentiable forest with sparse attention for the tabular data"):
  with members:
    - "litemort a memory efficient gradient boosting tree system on adaptive compact distributions"
    - "neural oblivious decision ensembles for deep learning on tabular data"
    - "optimization methods for interpretable differentiable decision trees in reinforcement learning"
    - "deep neural decision trees"
    - "quasi hyperbolic momentum and adam for deep learning"
    - "adaptive neural trees"
    - "catboost gradient boosting with categorical features support"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-4 (induced by source paper "linking social media posts to news with siamese transformers"):
  with members:
    - "adaptively sparse transformers"
    - "interactive attention for semantic text matching"
    - "understanding convolutional neural networks for text classification"
    - "neural article pair modeling for wikipedia sub article matching"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "convolutional neural networks for sentence classification"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-5 (induced by source paper "explicit sparse transformer concentrated attention through explicit selection"):
  with members:
    - "adaptively sparse transformers"
    - "muse parallel multi scale attention for sequence to sequence learning"
    - "pay less attention with lightweight and dynamic convolutions"
    - "joint source target self attention with locality constraints"
    - "transformer xl attentive language models beyond a fixed length context"
    - "fixup initialization residual learning without normalization"
    - "universal transformers"
    - "generating long sequences with sparse transformers"

Connected through hyperedge HE-6 (induced by source paper "sparse and structured visual attention"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "deep modular co attention networks for visual question answering"
    - "bilinear attention networks"
    - "neural baby talk"
    - "simnet stepwise image topic merging network for generating detailed and comprehensive image captions"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "sparse and constrained attention for neural machine translation"
    - "mutan multimodal tucker fusion for visual question answering"

Connected through hyperedge HE-7 (induced by source paper "adaptively sparse transformers"):
  with members:
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "ssn learning sparse switchable normalization via sparsestmax"
    - "bert rediscovers the classical nlp pipeline"
    - "pay less attention with lightweight and dynamic convolutions"
    - "attention is not explanation"
    - "generating long sequences with sparse transformers"
    - "selective attention for context aware neural machine translation"
    - "adaptive attention span in transformers"

Connected through hyperedge HE-8 (induced by source paper "dialogue transformers"):
  with members:
    - "multiwoz 2 1 multi domain dialogue state corrections and state tracking baselines"
    - "training neural response selection for task oriented dialogue systems"
    - "pretraining methods for dialog context representation learning"
    - "transformer xl attentive language models beyond a fixed length context"
    - "modeling intent dialog policies and response adaptation for goal oriented interactions"
    - "few shot generalization across dialogue tasks"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "wizard of wikipedia knowledge powered conversational agents"

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)