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: "recurrent neural networks as weighted language recognizers"
  Abstract: We investigate the computational complexity of various problems for simple recurrent neural networks (RNNs) as formal models for recognizing weighted languages. We focus on the single-layer, ReLU-activation, rational-weight RNNs with softmax, which are commonly used in natural language processing applications. We show that most problems for such RNNs are undecidable, including consistency, equivalence, minimization, and the determination of the highest-weighted string. However, for consistent RNNs the last problem becomes decidable, although the solution length can surpass all computable bounds. If additionally the string is limited to polynomial length, the problem becomes NP-complete and APX-hard. In summary, this shows that approximations and heuristic algorithms are necessary in practical applications of those RNNs.

Connected through hyperedge HE-1 (induced by source paper "consistency of a recurrent language model with respect to incomplete decoding"):
  with members:
    - "the curious case of neural text degeneration"
    - "neural text generation with unlikelihood training"
    - "on nmt search errors and model errors cat got your tongue"
    - "hierarchical neural story generation"
    - "correcting length bias in neural machine translation"
    - "on the state of the art of evaluation in neural language models"
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"

Connected through hyperedge HE-2 (induced by source paper "the curious case of neural text degeneration"):
  with members:
    - "unifying human and statistical evaluation for natural language generation"
    - "neural text generation with unlikelihood training"
    - "on nmt search errors and model errors cat got your tongue"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "on accurate evaluation of gans for language generation"
    - "eval all trust a few do wrong to none comparing sentence generation models"
    - "learning to write with cooperative discriminators"
    - "hierarchical neural story generation"

Connected through hyperedge HE-3 (induced by source paper "capacity bandwidth and compositionality in emergent language learning"):
  with members:
    - "linguistic generalization and compositionality in modern artificial neural networks"
    - "on the interaction between supervision and self play in emergent communication"
    - "anti efficient encoding in emergent communication"
    - "the compositionality of neural networks integrating symbolism and connectionism"
    - "word order biases in deep agent emergent communication"
    - "measuring compositionality in representation learning"
    - "countering language drift via visual grounding"
    - "compositional generalization through meta sequence to sequence learning"

Connected through hyperedge HE-4 (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"
    - "rational recurrences"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "on the turing completeness of modern neural network architectures"):
  with members:
    - "universal transformers"
    - "self attention with relative position representations"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "extensions and limitations of the neural gpu"
    - "attention is all you need"
    - "improving the neural gpu architecture for algorithm learning"
    - "identity mappings in deep residual networks"
    - "neural random access machines"

Connected through hyperedge HE-6 (induced by source paper "making ai meaningful again"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "from knowledge graph embedding to ontology embedding an analysis of the compatibility between vector space representations and rules"
    - "deep learning a critical appraisal"
    - "attention is all you need"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "hdltex hierarchical deep learning for text classification"
    - "measuring the tendency of cnns to learn surface statistical regularities"

Connected through hyperedge HE-7 (induced by source paper "on the computational power of rnns"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "the importance of being recurrent for modeling hierarchical structure"
    - "evaluating the ability of lstms to learn context free grammars"
    - "named entity recognition with bidirectional lstm cnns"
    - "semantically conditioned lstm based natural language generation for spoken dialogue systems"

Connected through hyperedge HE-8 (induced by source paper "sopa bridging cnns rnns and weighted finite state machines"):
  with members:
    - "recurrent additive networks"
    - "the effect of different writing tasks on linguistic style a case study of the roc story cloze task"
    - "convolutional sequence to sequence learning"
    - "quasi recurrent neural networks"
    - "rationalizing neural predictions"
    - "why should i trust you explaining the predictions of any classifier"
    - "understanding neural networks through representation erasure"
    - "improving hypernymy detection with an integrated path based and distributional method"

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.FL(Formal Languages and Automata Theory)