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: "compositional sequence labeling models for error detection in learner writing"
  Abstract: In this paper, we present the first experiments using neural network models for the task of error detection in learner writing. We perform a systematic comparison of alternative compositional architectures and propose a framework for error detection based on bidirectional LSTMs. Experiments on the CoNLL-14 shared task dataset show the model is able to outperform other participants on detecting errors in learner writing. Finally, the model is integrated with a publicly deployed self-assessment system, leading to performance comparable to human annotators.

Connected through hyperedge HE-1 (induced by source paper "linguistic knowledge and transferability of contextual representations"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "what can linguistics and deep learning contribute to each other"
    - "what do rnn language models learn about filler gap dependencies"
    - "what s going on in neural constituency parsers an analysis"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "why self attention a targeted evaluation of neural machine translation architectures"

Connected through hyperedge HE-2 (induced by source paper "multi task learning for argumentation mining"):
  with members:
    - "argumentation mining in user generated web discourse"
    - "lstm a search space odyssey"
    - "neural end to end learning for computational argumentation mining"
    - "multi task learning for mental health using social media text"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "attention strategies for multi source sequence to sequence learning"
    - "multitask learning for fine grained twitter sentiment analysis"
    - "cross lingual and cross domain discourse segmentation of entire documents"

Connected through hyperedge HE-3 (induced by source paper "context is key grammatical error detection with contextual word representations"):
  with members:
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "wronging a right generating better errors to improve grammatical error detection"
    - "jfleg a fluency corpus and benchmark for grammatical error correction"
    - "semi supervised multitask learning for sequence labeling"
    - "semi supervised sequence tagging with bidirectional language models"
    - "auxiliary objectives for neural error detection models"

Connected through hyperedge HE-4 (induced by source paper "multi task learning for chinese word usage errors detection"):
  with members:
    - "multi task learning of keyphrase boundary classification"
    - "semi supervised multitask learning for sequence labeling"
    - "auxiliary objectives for neural error detection models"
    - "adversarial multi task learning for text classification"
    - "an overview of multi task learning in deep neural networks"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "when is multitask learning effective semantic sequence prediction under varying data conditions"
    - "multi task cross lingual sequence tagging from scratch"

Connected through hyperedge HE-5 (induced by source paper "jointly learning to label sentences and tokens"):
  with members:
    - "zero shot sequence labeling transferring knowledge from sentences to tokens"
    - "jfleg a fluency corpus and benchmark for grammatical error correction"
    - "on the state of the art of evaluation in neural language models"
    - "semi supervised multitask learning for sequence labeling"
    - "convolutional sequence to sequence learning"
    - "neural attention models for sequence classification analysis and application to key term extraction and dialogue act detection"
    - "neural machine translation with supervised attention"
    - "language modeling with gated convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "reaching human level performance in automatic grammatical error correction an empirical study"):
  with members:
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "near human level performance in grammatical error correction with hybrid machine translation"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"
    - "inherent biases in reference based evaluation for grammatical error correction and text simplification"
    - "joint training for neural machine translation models with monolingual data"
    - "jfleg a fluency corpus and benchmark for grammatical error correction"
    - "attention is all you need"
    - "adapting sequence models for sentence correction"

Connected through hyperedge HE-7 (induced by source paper "wronging a right generating better errors to improve grammatical error detection"):
  with members:
    - "semi supervised multitask learning for sequence labeling"
    - "phrase based machine translation is state of the art for automatic grammatical error correction"
    - "attending to characters in neural sequence labeling models"
    - "improving neural machine translation models with monolingual data"
    - "neural machine translation by jointly learning to align and translate"
    - "grammar as a foreign language"

Connected through hyperedge HE-8 (induced by source paper "interpretable adversarial perturbation in input embedding space for text"):
  with members:
    - "towards crafting text adversarial samples"
    - "adversarial examples for evaluating reading comprehension systems"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"
    - "intriguing properties of neural networks"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"

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)