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 word and dependency path embeddings for aspect term extraction"
  Abstract: In this paper, we develop a novel approach to aspect term extraction based on unsupervised learning of distributed representations of words and dependency paths. The basic idea is to connect two words (w1 and w2) with the dependency path (r) between them in the embedding space. Specifically, our method optimizes the objective w1 + r = w2 in the low-dimensional space, where the multi-hop dependency paths are treated as a sequence of grammatical relations and modeled by a recurrent neural network. Then, we design the embedding features that consider linear context and dependency context information, for the conditional random field (CRF) based aspect term extraction. Experimental results on the SemEval datasets show that, (1) with only embedding features, we can achieve state-of-the-art results; (2) our embedding method which incorporates the syntactic information among words yields better performance than other representative ones in aspect term extraction.

Connected through hyperedge HE-1 (induced by source paper "comprehensive analysis of aspect term extraction methods using various text embeddings"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "advances in pre training distributed word representations"
    - "conceptnet at semeval 2017 task 2 extending word embeddings with multilingual relational knowledge"
    - "long term memory networks for question answering"

Connected through hyperedge HE-2 (induced by source paper "doer dual cross shared rnn for aspect term polarity co extraction"):
  with members:
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "transformation networks for target oriented sentiment classification"
    - "a unified model for opinion target extraction and target sentiment prediction"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "improving aspect term extraction with bidirectional dependency tree representation"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "learning to attend via word aspect associative fusion for aspect based sentiment analysis"
    - "interactive attention networks for aspect level sentiment classification"

Connected through hyperedge HE-3 (induced by source paper "pod positional dependency based word embedding for aspect term extraction"):
  with members:
    - "language models with transformers"
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "self attention with relative position representations"
    - "deep contextualized word representations"
    - "aspect term extraction with history attention and selective transformation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "elixa a modular and flexible absa platform"

Connected through hyperedge HE-4 (induced by source paper "integrating deep learning with logic fusion for information extraction"):
  with members:
    - "neural logic machines"
    - "deepproblog neural probabilistic logic programming"
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "deep probabilistic logic a unifying framework for indirect supervision"
    - "joint entity recognition and relation extraction as a multi head selection problem"
    - "adversarially regularising neural nli models to integrate logical background knowledge"
    - "adversarial training for multi context joint entity and relation extraction"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "controlled cnn based sequence labeling for aspect extraction"):
  with members:
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "aspect term extraction with history attention and selective transformation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "incremental learning through deep adaptation"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "convolutional sequence to sequence learning"
    - "recursive neural conditional random fields for aspect based sentiment analysis"

Connected through hyperedge HE-6 (induced by source paper "an interactive multi task learning network for end to end aspect based sentiment analysis"):
  with members:
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "transformation networks for target oriented sentiment classification"
    - "summarizing opinions aspect extraction meets sentiment prediction and they are both weakly supervised"
    - "exploiting document knowledge for aspect level sentiment classification"
    - "a unified model for opinion target extraction and target sentiment prediction"
    - "aspect term extraction with history attention and selective transformation"
    - "learning to attend via word aspect associative fusion for aspect based sentiment analysis"
    - "recursive neural conditional random fields for aspect based sentiment analysis"

Connected through hyperedge HE-7 (induced by source paper "aspect specific opinion expression extraction using attention based lstm crf network"):
  with members:
    - "recursive neural conditional random fields for aspect based sentiment analysis"
    - "aspect level sentiment classification with deep memory network"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"
    - "effective lstms for target dependent sentiment classification"
    - "neural machine translation by jointly learning to align and translate"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "aspect detection using word and char embeddings with bi lstm and crf"):
  with members:
    - "method for aspect based sentiment annotation using rhetorical analysis"
    - "conceptnet at semeval 2017 task 2 extending word embeddings with multilingual relational knowledge"
    - "recursive neural conditional random fields for aspect based sentiment analysis"
    - "a hierarchical model of reviews for aspect based sentiment analysis"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "enriching word vectors with subword information"
    - "neural architectures for named entity recognition"
    - "character level convolutional networks for text classification"

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)