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: "adversarial multi criteria learning for chinese word segmentation"
  Abstract: Different linguistic perspectives causes many diverse segmentation criteria for Chinese word segmentation (CWS). Most existing methods focus on improve the performance for each single criterion. However, it is interesting to exploit these different criteria and mining their common underlying knowledge. In this paper, we propose adversarial multi-criteria learning for CWS by integrating shared knowledge from multiple heterogeneous segmentation criteria. Experiments on eight corpora with heterogeneous segmentation criteria show that the performance of each corpus obtains a significant improvement, compared to single-criterion learning. Source codes of this paper are available on Github.

Connected through hyperedge HE-1 (induced by source paper "integrating boundary assembling into a dnn framework for named entity recognition in chinese social media text"):
  with members:
    - "chinese ner using lattice lstm"
    - "learning word segmentation representations to improve named entity recognition for chinese social media"
    - "f score driven max margin neural network for named entity recognition in chinese social media"
    - "neural word segmentation learning for chinese"
    - "improving named entity recognition for chinese social media with word segmentation representation learning"
    - "neural architectures for named entity recognition"
    - "named entity recognition with bidirectional lstm cnns"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "glyce glyph vectors for chinese character representations"):
  with members:
    - "toward fast and accurate neural chinese word segmentation with multi criteria learning"
    - "chinese ner using lattice lstm"
    - "subword encoding in lattice lstm for chinese word segmentation"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "deep contextualized word representations"
    - "state of the art chinese word segmentation with bi lstms"
    - "prior knowledge integration for neural machine translation using posterior regularization"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "go from the general to the particular multi domain translation with domain transformation networks"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "multilingual neural machine translation with knowledge distillation"
    - "improving domain adaptation translation with domain invariant and specific information"
    - "massively multilingual adversarial speech recognition"
    - "a survey of domain adaptation for neural machine translation"
    - "domain adversarial training for accented speech recognition"
    - "translating pro drop languages with reconstruction models"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "dsreg using distant supervision as a regularizer"):
  with members:
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "syntactic scaffolds for semantic structures"
    - "hybrid semi markov crf for neural sequence labeling"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "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-5 (induced by source paper "is word segmentation necessary for deep learning of chinese representations"):
  with members:
    - "subword encoding in lattice lstm for chinese word segmentation"
    - "prior knowledge integration for neural machine translation using posterior regularization"
    - "bag of words as target for neural machine translation"
    - "attention is all you need"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "modeling source syntax for neural machine translation"
    - "fast and accurate neural word segmentation for chinese"
    - "which encoding is the best for text classification in chinese english japanese and korean"

Connected through hyperedge HE-6 (induced by source paper "attention is all you need for chinese word segmentation"):
  with members:
    - "self attention with relative position representations"
    - "switch lstms for multi criteria chinese word segmentation"
    - "attention is all you need"
    - "convolutional neural network with word embeddings for chinese word segmentation"
    - "fast and accurate neural word segmentation for chinese"
    - "a structured self attentive sentence embedding"
    - "neural word segmentation with rich pretraining"
    - "dag based long short term memory for neural word segmentation"

Connected through hyperedge HE-7 (induced by source paper "improving domain adaptation translation with domain invariant and specific information"):
  with members:
    - "attention is all you need"
    - "an empirical comparison of simple domain adaptation methods for neural machine translation"
    - "convolutional sequence to sequence learning"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "domain control for neural machine translation"
    - "guided alignment training for topic aware neural machine translation"
    - "character level convolutional networks for text classification"
    - "improving neural machine translation models with monolingual data"

Connected through hyperedge HE-8 (induced by source paper "toward fast and accurate neural chinese word segmentation with multi criteria learning"):
  with members:
    - "subword encoding in lattice lstm for chinese word segmentation"
    - "know what you don t know unanswerable questions for squad"
    - "coqa a conversational question answering challenge"
    - "switch lstms for multi criteria chinese word segmentation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "neural word segmentation with rich pretraining"
    - "multi task domain adaptation for sequence tagging"

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)