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: "learning joint semantic parsers from disjoint data"
  Abstract: We present a new approach to learning semantic parsers from multiple datasets, even when the target semantic formalisms are drastically different, and the underlying corpora do not overlap. We handle such "disjoint" data by treating annotations for unobserved formalisms as latent structured variables. Building on state-of-the-art baselines, we show improvements both in frame-semantic parsing and semantic dependency parsing by modeling them jointly.

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"
    - "rational recurrences"
    - "bottom up abstractive summarization"
    - "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"
    - "rational recurrences"
    - "syntactic scaffolds for semantic structures"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "broad coverage semantic parsing as transduction"):
  with members:
    - "core semantic first a top down approach for amr parsing"
    - "compositional semantic parsing across graphbanks"
    - "semeval 2019 task 1 cross lingual semantic parsing with ucca"
    - "rewarding smatch transition based amr parsing with reinforcement learning"
    - "hlt suda at semeval 2019 task 1 ucca graph parsing as constituent tree parsing"
    - "multitask parsing across semantic representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "amr parsing as graph prediction with latent alignment"

Connected through hyperedge HE-4 (induced by source paper "a syntax aware multi task learning framework for chinese semantic role labeling"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "syntax enhanced neural machine translation with syntax aware word representations"
    - "syntax aware neural semantic role labeling"
    - "syntactic scaffolds for semantic structures"
    - "a span selection model for semantic role labeling"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "linguistically informed self attention for semantic role labeling"

Connected through hyperedge HE-5 (induced by source paper "positional attention based frame identification with bert a deep learning approach to target disambiguation and semantic frame selection"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "attention is all you need"
    - "advances in pre training distributed word representations"
    - "frame semantic parsing with softmax margin segmental rnns and a syntactic scaffold"
    - "interactive attention networks for aspect level sentiment classification"

Connected through hyperedge HE-6 (induced by source paper "dependency or span end to end uniform semantic role labeling"):
  with members:
    - "i know what you want semantic learning for text comprehension"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "a simple and accurate syntax agnostic neural model for dependency based semantic role labeling"
    - "end to end neural coreference resolution"
    - "deep semantic role labeling with self attention"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "neural semantic role labeling with dependency path embeddings"

Connected through hyperedge HE-7 (induced by source paper "compositional semantic parsing across graphbanks"):
  with members:
    - "amr parsing as sequence to graph transduction"
    - "parser training with heterogeneous treebanks"
    - "multitask parsing across semantic representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "amr dependency parsing with a typed semantic algebra"
    - "amr parsing as graph prediction with latent alignment"
    - "simpler but more accurate semantic dependency parsing"
    - "robust incremental neural semantic graph parsing"

Connected through hyperedge HE-8 (induced by source paper "dependency and span cross style semantic role labeling on propbank and nombank"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "constituency parsing with a self attentive encoder"
    - "linguistically informed self attention for semantic role labeling"
    - "a simple and accurate syntax agnostic neural model for dependency based semantic role labeling"
    - "encoding sentences with graph convolutional networks 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)