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: "stochastic answer networks for natural language inference"
  Abstract: We propose a stochastic answer network (SAN) to explore multi-step inference strategies in Natural Language Inference. Rather than directly predicting the results given the inputs, the model maintains a state and iteratively refines its predictions. Our experiments show that SAN achieves the state-of-the-art results on three benchmarks: Stanford Natural Language Inference (SNLI) dataset, MultiGenre Natural Language Inference (MultiNLI) dataset and Quora Question Pairs dataset.

Connected through hyperedge HE-1 (induced by source paper "the microsoft toolkit of multi task deep neural networks for natural language understanding"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "multi task deep neural networks for natural language understanding"
    - "smart robust and efficient fine tuning for pre trained natural language models through principled regularized optimization"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "freelb enhanced adversarial training for language understanding"

Connected through hyperedge HE-2 (induced by source paper "smart robust and efficient fine tuning for pre trained natural language models through principled regularized optimization"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "parameter efficient transfer learning for nlp"
    - "spanbert improving pre training by representing and predicting spans"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "on the variance of the adaptive learning rate and beyond"
    - "a surprisingly robust trick for the winograd schema challenge"
    - "multi task deep neural networks for natural language understanding"
    - "albert a lite bert for self supervised learning of language representations"

Connected through hyperedge HE-3 (induced by source paper "improving textual network embedding with global attention via optimal transport"):
  with members:
    - "improving sequence to sequence learning via optimal transport"
    - "gromov wasserstein alignment of word embedding spaces"
    - "adversarial text generation via feature mover s distance"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "improved semantic aware network embedding with fine grained word alignment"
    - "joint embedding of words and labels for text classification"
    - "diffusion maps for textual network embedding"
    - "network representation learning a survey"

Connected through hyperedge HE-4 (induced by source paper "multi perspective inferrer reasoning sentences relationship from holistic perspective"):
  with members:
    - "dynamic layer aggregation for neural machine translation with routing by agreement"
    - "dynamic past and future for neural machine translation"
    - "multi labeled relation extraction with attentive capsule network"
    - "hypothesis only baselines in natural language inference"
    - "information aggregation via dynamic routing for sequence encoding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "dr bilstm dependent reading bidirectional lstm for natural language inference"

Connected through hyperedge HE-5 (induced by source paper "multi task deep neural networks for natural language understanding"):
  with members:
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "unified language model pre training for natural language understanding and generation"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "neural approaches to conversational ai"

Connected through hyperedge HE-6 (induced by source paper "simple and effective text matching with richer alignment features"):
  with members:
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "natural language inference over interaction space"
    - "neural paraphrase identification of questions with noisy pretraining"
    - "bilateral multi perspective matching for natural language sentences"
    - "a decomposable attention model for natural language inference"
    - "enhanced lstm for natural language inference"
    - "key value memory networks for directly reading documents"

Connected through hyperedge HE-7 (induced by source paper "conclusion supplement answer generation for non factoid questions"):
  with members:
    - "document expansion by query prediction"
    - "multi passage machine reading comprehension with cross passage answer verification"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "attention is all you need"
    - "s net from answer extraction to answer generation for machine reading comprehension"
    - "deep reinforcement learning for dialogue generation"
    - "review networks for caption generation"

Connected through hyperedge HE-8 (induced by source paper "conflict as an inverse of attention in sequence relationship"):
  with members:
    - "deep contextualized word representations"
    - "attention is all you need"
    - "neural paraphrase identification of questions with noisy pretraining"
    - "bilateral multi perspective matching for natural language sentences"
    - "deep ctr prediction in display advertising"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"

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)