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: "rational recurrences"
  Abstract: Despite the tremendous empirical success of neural models in natural language processing, many of them lack the strong intuitions that accompany classical machine learning approaches. Recently, connections have been shown between convolutional neural networks (CNNs) and weighted finite state automata (WFSAs), leading to new interpretations and insights. In this work, we show that some recurrent neural networks also share this connection to WFSAs. We characterize this connection formally, defining rational recurrences to be recurrent hidden state update functions that can be written as the Forward calculation of a finite set of WFSAs. We show that several recent neural models use rational recurrences. Our analysis provides a fresh view of these models and facilitates devising new neural architectures that draw inspiration from WFSAs. We present one such model, which performs better than two recent baselines on language modeling and text classification. Our results demonstrate that transferring intuitions from classical models like WFSAs can be an effective approach to designing and understanding neural models.

Connected through hyperedge HE-1 (induced by source paper "text generation with exemplar based adaptive decoding"):
  with members:
    - "retrieve and refine improved sequence generation models for dialogue"
    - "backpropagating through structured argmax using a spigot"
    - "generating diverse and accurate visual captions by comparative adversarial learning"
    - "bottom up abstractive summarization"
    - "learning joint semantic parsers from disjoint data"
    - "learning neural templates for text generation"
    - "contextual parameter generation for universal neural machine translation"
    - "contextual explanation networks"

Connected through hyperedge HE-2 (induced by source paper "palm a hybrid parser and language model"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "unsupervised latent tree induction with deep inside outside recursive autoencoders"
    - "transformer xl attentive language models beyond a fixed length context"
    - "direct output connection for a high rank language model"
    - "backpropagating through structured argmax using a spigot"
    - "syntactic scaffolds for semantic structures"
    - "learning joint semantic parsers from disjoint data"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "a lightweight recurrent network for sequence modeling"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "accelerating neural transformer via an average attention network"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "simple recurrent units for highly parallelizable recurrence"
    - "deriving neural architectures from sequence and graph kernels"
    - "recurrent additive networks"
    - "breaking the softmax bottleneck a high rank rnn language model"

Connected through hyperedge HE-4 (induced by source paper "rnn architecture learning with sparse regularization"):
  with members:
    - "energy and policy considerations for deep learning in nlp"
    - "green ai"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "darts differentiable architecture search"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "snip single shot network pruning based on connection sensitivity"
    - "simple recurrent units for highly parallelizable recurrence"
    - "intelligible language modeling with input switched affine networks"

Connected through hyperedge HE-5 (induced by source paper "interpreting and improving natural language processing in machines with natural language processing in the brain"):
  with members:
    - "assessing bert s syntactic abilities"
    - "transformer xl attentive language models beyond a fixed length context"
    - "finding syntax in human encephalography with beam search"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "sequential neural networks as automata"):
  with members:
    - "investigating capsule networks with dynamic routing for text classification"
    - "context free transductions with neural stacks"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "attention is all you need"
    - "regularizing deep neural networks by noise its interpretation and optimization"
    - "effective approaches to attention based neural machine translation"
    - "character aware neural language models"
    - "attention based models for speech recognition"

Connected through hyperedge HE-7 (induced by source paper "complexity bounds for relational algebra over document spanners"):
  with members:
    - "constant delay enumeration for nondeterministic document spanners"
    - "constant delay algorithms for regular document spanners"
    - "document spanners for extracting incomplete information expressiveness and complexity"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "enumeration complexity of conjunctive queries with functional dependencies"
    - "joining extractions of regular expressions"
    - "recursive programs for document spanners"
    - "semantics and complexity of sparql"

Connected through hyperedge HE-8 (induced by source paper "generating diverse and accurate visual captions by comparative adversarial learning"):
  with members:
    - "netizen style commenting on fashion photos dataset and diversity measures"
    - "discriminability objective for training descriptive captions"
    - "diverse and coherent paragraph generation from images"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "improved image captioning via policy gradient optimization of spider"
    - "attention is all you need"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "speaking the same language matching machine to human captions by adversarial training"

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)