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: "finding syntax in human encephalography with beam search"
  Abstract: Recurrent neural network grammars (RNNGs) are generative models of (tree,string) pairs that rely on neural networks to evaluate derivational choices. Parsing with them using beam search yields a variety of incremental complexity metrics such as word surprisal and parser action count. When used as regressors against human electrophysiological responses to naturalistic text, they derive two amplitude effects: an early peak and a P600-like later peak. By contrast, a non-syntactic neural language model yields no reliable effects. Model comparisons attribute the early peak to syntactic composition within the RNNG. This pattern of results recommends the RNNG+beam search combination as a mechanistic model of the syntactic processing that occurs during normal human language comprehension.

Connected through hyperedge HE-1 (induced by source paper "the fluidity of concept representations in human brain signals"):
  with members:
    - "robust evaluation of language brain encoding experiments"
    - "analyzing and interpreting neural networks for nlp a report on the first blackboxnlp workshop"
    - "advancing nlp with cognitive language processing signals"
    - "does the brain represent words an evaluation of brain decoding studies of language understanding"
    - "multimodal grounding for language processing"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "experiential distributional and dependency based word embeddings have complementary roles in decoding brain activity"

Connected through hyperedge HE-2 (induced by source paper "unsupervised recurrent neural network grammars"):
  with members:
    - "unsupervised latent tree induction with deep inside outside recursive autoencoders"
    - "cooperative learning of disjoint syntax and semantics"
    - "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 "learning to collocate neural modules for image captioning"):
  with members:
    - "unpaired image captioning via scene graph alignments"
    - "scene graph generation with external knowledge and image reconstruction"
    - "context aware visual policy network for fine grained image captioning"
    - "context and attribute grounded dense captioning"
    - "bilinear attention networks"
    - "neural baby talk"
    - "learning to assemble neural module tree networks for visual grounding"
    - "context aware visual policy network for sequence level image captioning"

Connected through hyperedge HE-4 (induced by source paper "scalable syntax aware language models using knowledge distillation"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "assessing bert s syntactic abilities"
    - "universal transformers"
    - "distilling knowledge for search based structured prediction"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "the importance of being recurrent for modeling hierarchical structure"

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"
    - "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"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "cross domain generalization of neural constituency parsers"):
  with members:
    - "extending a parser to distant domains using a few dozen partially annotated examples"
    - "multilingual constituency parsing with self attention and pre training"
    - "deep contextualized word representations"
    - "straight to the tree constituency parsing with neural syntactic distance"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "constituency parsing with a self attentive encoder"
    - "in order transition based constituent parsing"
    - "a minimal span based neural constituency parser"

Connected through hyperedge HE-7 (induced by source paper "neural language models as psycholinguistic subjects representations of syntactic state"):
  with members:
    - "a neural model of adaptation in reading"
    - "what do rnn language models learn about filler gap dependencies"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "colorless green recurrent networks dream hierarchically"
    - "targeted syntactic evaluation of language models"
    - "exploring the syntactic abilities of rnns with multi task learning"
    - "effective inference for generative neural parsing"

Connected through hyperedge HE-8 (induced by source paper "advancing nlp with cognitive language processing signals"):
  with members:
    - "entity recognition at first sight improving ner with eye movement information"
    - "eth ds3lab at semeval 2018 task 7 effectively combining recurrent and convolutional neural networks for relation classification and extraction"
    - "replicability analysis for natural language processing testing significance with multiple datasets"
    - "assessing state of the art sentiment models on state of the art sentiment datasets"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "leveraging cognitive features for sentiment analysis"
    - "what to do about non standard or non canonical language in nlp"
    - "improving sentence compression by learning to predict gaze"

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)