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: "syntactic scaffolds for semantic structures"
  Abstract: We introduce the syntactic scaffold, an approach to incorporating syntactic information into semantic tasks. Syntactic scaffolds avoid expensive syntactic processing at runtime, only making use of a treebank during training, through a multitask objective. We improve over strong baselines on PropBank semantics, frame semantics, and coreference resolution, achieving competitive performance on all three tasks.

Connected through hyperedge HE-1 (induced by source paper "generalizing natural language analysis through span relation representations"):
  with members:
    - "enriching pre trained language model with entity information for relation classification"
    - "bert rediscovers the classical nlp pipeline"
    - "spanbert improving pre training by representing and predicting spans"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "multi task deep neural networks for natural language understanding"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "simple bert models for relation extraction and semantic role labeling"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"

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"
    - "learning joint semantic parsers from disjoint data"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "dsreg using distant supervision as a regularizer"):
  with members:
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "hybrid semi markov crf for neural sequence labeling"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "attention is all you need"
    - "learning with noise enhance distantly supervised relation extraction with dynamic transition matrix"
    - "the narrativeqa reading comprehension challenge"

Connected through hyperedge HE-4 (induced by source paper "transfer fine tuning a bert case study"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "style transfer through back translation"
    - "deep contextualized word representations"
    - "an efficient framework for learning sentence representations"
    - "learning general purpose distributed sentence representations via large scale multi task learning"

Connected through hyperedge HE-5 (induced by source paper "a neural entity coreference resolution review"):
  with members:
    - "context in neural machine translation a review of models and evaluations"
    - "incorporating context and external knowledge for pronoun coreference resolution"
    - "gendered ambiguous pronouns shared task boosting model confidence by evidence pooling"
    - "knowledge aware pronoun coreference resolution"
    - "measuring bias in contextualized word representations"
    - "improving generalization in coreference resolution via adversarial training"
    - "look again at the syntax relational graph convolutional network for gendered ambiguous pronoun resolution"
    - "gender bias in contextualized word embeddings"

Connected through hyperedge HE-6 (induced by source paper "faster shift reduce constituent parsing with a non binary bottom up strategy"):
  with members:
    - "what s going on in neural constituency parsers an analysis"
    - "a tree based decoder for neural machine translation"
    - "constituency parsing with a self attentive encoder"
    - "efficient dependency guided named entity recognition"
    - "in order transition based constituent parsing"
    - "a minimal span based neural constituency parser"
    - "towards string to tree neural machine translation"
    - "modeling source syntax for neural machine translation"

Connected through hyperedge HE-7 (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"
    - "learning joint semantic parsers from disjoint data"
    - "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-8 (induced by source paper "syntactically supervised transformers for faster neural machine translation"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "unsupervised latent tree induction with deep inside outside recursive autoencoders"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "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"

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)