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: "unsupervised aspect term extraction with b lstm crf using automatically labelled datasets"
  Abstract: Aspect Term Extraction (ATE) identifies opinionated aspect terms in texts and is one of the tasks in the SemEval Aspect Based Sentiment Analysis (ABSA) contest. The small amount of available datasets for supervised ATE and the costly human annotation for aspect term labelling give rise to the need for unsupervised ATE. In this paper, we introduce an architecture that achieves top-ranking performance for supervised ATE. Moreover, it can be used efficiently as feature extractor and classifier for unsupervised ATE. Our second contribution is a method to automatically construct datasets for ATE. We train a classifier on our automatically labelled datasets and evaluate it on the human annotated SemEval ABSA test sets. Compared to a strong rule-based baseline, we obtain a dramatically higher F-score and attain precision values above 80%. Our unsupervised method beats the supervised ABSA baseline from SemEval, while preserving high precision scores.

Connected through hyperedge HE-1 (induced by source paper "hamner headword amplified multi span distantly supervised method for domain specific named entity recognition"):
  with members:
    - "multi grained named entity recognition"
    - "a survey on deep learning for named entity recognition"
    - "a span selection model for semantic role labeling"
    - "deep contextualized word representations"
    - "automated phrase mining from massive text corpora"
    - "a structured self attentive sentence embedding"
    - "swellshark a generative model for biomedical named entity recognition without labeled data"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-2 (induced by source paper "improving aspect term extraction with bidirectional dependency tree representation"):
  with members:
    - "lifelong learning crf for supervised aspect extraction"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "multi task memory networks for category specific aspect and opinion terms co extraction"
    - "unsupervised word and dependency path embeddings for aspect term extraction"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "bidirectional tree structured lstm with head lexicalization"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-3 (induced by source paper "learning named entity tagger using domain specific dictionary"):
  with members:
    - "cross type biomedical named entity recognition with deep multi task learning"
    - "automated phrase mining from massive text corpora"
    - "swellshark a generative model for biomedical named entity recognition without labeled data"
    - "empower sequence labeling with task aware neural language model"
    - "recurrent neural network models for disease name recognition using domain invariant features"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-4 (induced by source paper "dataset construction via attention for aspect term extraction with distant supervision"):
  with members:
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "recent trends in deep learning based natural language processing"
    - "a structured self attentive sentence embedding"
    - "unsupervised word and dependency path embeddings for aspect term extraction"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

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)