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: "unsupervised latent tree induction with deep inside outside recursive autoencoders"
  Abstract: We introduce deep inside-outside recursive autoencoders (DIORA), a fully-unsupervised method for discovering syntax that simultaneously learns representations for constituents within the induced tree. Our approach predicts each word in an input sentence conditioned on the rest of the sentence and uses inside-outside dynamic programming to consider all possible binary trees over the sentence. At test time the CKY algorithm extracts the highest scoring parse. DIORA achieves a new state-of-the-art F1 in unsupervised binary constituency parsing (unlabeled) in two benchmark datasets, WSJ and MultiNLI.

Connected through hyperedge HE-1 (induced by source paper "are pre trained language models aware of phrases simple but strong baselines for grammar induction"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "inducing syntactic trees from bert representations"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "compound probabilistic context free grammars for grammar induction"
    - "from balustrades to pierre vinken looking for syntax in transformer self attentions"
    - "assessing bert s syntactic abilities"
    - "an imitation learning approach to unsupervised parsing"
    - "a critical analysis of biased parsers in unsupervised parsing"

Connected through hyperedge HE-2 (induced by source paper "unsupervised recurrent neural network grammars"):
  with members:
    - "cooperative learning of disjoint syntax and semantics"
    - "finding syntax in human encephalography with beam search"
    - "on tree based neural sentence modeling"
    - "unsupervised grammar induction with depth bounded pcfg"
    - "backpropagating through structured argmax using a spigot"
    - "inducing grammars with and for neural machine translation"
    - "latent tree learning with differentiable parsers shift reduce parsing and chart parsing"
    - "towards dynamic computation graphs via sparse latent structure"

Connected through hyperedge HE-3 (induced by source paper "compound probabilistic context free grammars for grammar induction"):
  with members:
    - "scalable syntax aware language models using knowledge distillation"
    - "unsupervised recurrent neural network grammars"
    - "a multi task approach for disentangling syntax and semantics in sentence representations"
    - "an imitation learning approach to unsupervised parsing"
    - "text generation with exemplar based adaptive decoding"
    - "controllable paraphrase generation with a syntactic exemplar"
    - "visually grounded neural syntax acquisition"
    - "structural supervision improves learning of non local grammatical dependencies"

Connected through hyperedge HE-4 (induced by source paper "palm a hybrid parser and language model"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "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"
    - "rational recurrences"
    - "syntactic scaffolds for semantic structures"
    - "learning joint semantic parsers from disjoint data"
    - "deep contextualized word representations"

Connected through hyperedge HE-5 (induced by source paper "syntactically supervised transformers for faster neural machine translation"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "syntactic scaffolds for semantic structures"
    - "blockwise parallel decoding for deep autoregressive models"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "a call for clarity in reporting bleu scores"
    - "linguistically informed self attention for semantic role labeling"

Connected through hyperedge HE-6 (induced by source paper "tree transformer integrating tree structures into self attention"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "compound probabilistic context free grammars for grammar induction"
    - "linguistic knowledge and transferability of contextual representations"
    - "visualizing attention in transformer based language representation models"
    - "unified language model pre training for natural language understanding and generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "linguistically informed self attention for semantic role labeling"

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)