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: "segmental recurrent neural networks"
  Abstract: We introduce segmental recurrent neural networks (SRNNs) which define, given an input sequence, a joint probability distribution over segmentations of the input and labelings of the segments. Representations of the input segments (i.e., contiguous subsequences of the input) are computed by encoding their constituent tokens using bidirectional recurrent neural nets, and these "segment embeddings" are used to define compatibility scores with output labels. These local compatibility scores are integrated using a global semi-Markov conditional random field. Both fully supervised training -- in which segment boundaries and labels are observed -- as well as partially supervised training -- in which segment boundaries are latent -- are straightforward. Experiments on handwriting recognition and joint Chinese word segmentation/POS tagging show that, compared to models that do not explicitly represent segments such as BIO tagging schemes and connectionist temporal classification (CTC), SRNNs obtain substantially higher accuracies.

Connected through hyperedge HE-1 (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"
    - "learning joint semantic parsers from disjoint data"

Connected through hyperedge HE-2 (induced by source paper "improving human text comprehension through semi markov crf based neural section title generation"):
  with members:
    - "on the convergence of adam and beyond"
    - "bottom up abstractive summarization"
    - "deep contextualized word representations"
    - "neural document summarization by jointly learning to score and select sentences"
    - "learning recurrent span representations for extractive question answering"
    - "get to the point summarization with pointer generator networks"
    - "challenges in data to document generation"
    - "multitask learning with ctc and segmental crf for speech recognition"

Connected through hyperedge HE-3 (induced by source paper "latent variable generative models for data efficient text classification"):
  with members:
    - "smaller text classifiers with discriminative cluster embeddings"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "structured attention networks"
    - "recurrent neural network grammars"
    - "very deep convolutional networks for text classification"
    - "efficient character level document classification by combining convolution and recurrent layers"
    - "importance weighted autoencoders"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-4 (induced by source paper "tensorflow eager a multi stage python embedded dsl for machine learning"):
  with members:
    - "don t unroll adjoint differentiating ssa form programs"
    - "dlvm a modern compiler infrastructure for deep learning systems"
    - "deep residual learning for image recognition"
    - "automatic differentiation in machine learning a survey"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "cudnn efficient primitives for deep learning"

Connected through hyperedge HE-5 (induced by source paper "learning explicit and implicit structures for targeted sentiment analysis"):
  with members:
    - "aspect based sentiment analysis with gated convolutional networks"
    - "aspect level sentiment classification with deep memory network"
    - "neural architectures for named entity recognition"
    - "effective lstms for target dependent sentiment classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "subword level language identification for intra word code switching"):
  with members:
    - "a fast compact accurate model for language identification of codemixed text"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "adam a method for stochastic optimization"
    - "unsupervised discovery of morphemes"

Connected through hyperedge HE-7 (induced by source paper "bayesian online detection and prediction of change points"):
  with members:
    - "selective review of offline change point detection methods"

Connected through hyperedge HE-8 (induced by source paper "syntactic scaffolds for semantic structures"):
  with members:
    - "learning joint semantic parsers from disjoint data"
    - "multitask parsing across semantic representations"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "end to end neural coreference resolution"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "deep semantic role labeling with self attention"

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)