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: "effective lstms for target dependent sentiment classification"
  Abstract: Target-dependent sentiment classification remains a challenge: modeling the semantic relatedness of a target with its context words in a sentence. Different context words have different influences on determining the sentiment polarity of a sentence towards the target. Therefore, it is desirable to integrate the connections between target word and context words when building a learning system. In this paper, we develop two target dependent long short-term memory (LSTM) models, where target information is automatically taken into account. We evaluate our methods on a benchmark dataset from Twitter. Empirical results show that modeling sentence representation with standard LSTM does not perform well. Incorporating target information into LSTM can significantly boost the classification accuracy. The target-dependent LSTM models achieve state-of-the-art performances without using syntactic parser or external sentiment lexicons.

Connected through hyperedge HE-1 (induced by source paper "description based text classification with reinforcement learning"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "docbert bert for document classification"
    - "question answering is a format when is it useful"
    - "entity relation extraction as multi turn question answering"
    - "integrating semantic knowledge to tackle zero shot text classification"
    - "coreference resolution as query based span prediction"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"

Connected through hyperedge HE-2 (induced by source paper "discovering protagonist of sentiment with aspect reconstructed capsule network"):
  with members:
    - "a capsule network for recommendation and explaining what you like and dislike"
    - "multi labeled relation extraction with attentive capsule network"
    - "transformation networks for target oriented sentiment classification"
    - "aspect level sentiment classification with attention over attention neural networks"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "dynamic routing between capsules"
    - "interactive attention networks for aspect level sentiment classification"
    - "aspect level sentiment classification with deep memory network"

Connected through hyperedge HE-3 (induced by source paper "latent opinions transfer network for target oriented opinion words extraction"):
  with members:
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "attention is all you need"
    - "recursive neural conditional random fields for aspect based sentiment analysis"
    - "a convolutional encoder model for neural machine translation"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-4 (induced by source paper "a novel aspect guided deep transition model for aspect based sentiment analysis"):
  with members:
    - "context aware embedding for targeted aspect based sentiment analysis"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "open domain targeted sentiment analysis via span based extraction and classification"
    - "an interactive multi task learning network for end to end aspect based sentiment analysis"
    - "neural aspect and opinion term extraction with mined rules as weak supervision"
    - "multi task learning for multi modal emotion recognition and sentiment analysis"
    - "doer dual cross shared rnn for aspect term polarity co extraction"
    - "latent variable sentiment grammar"

Connected through hyperedge HE-5 (induced by source paper "selective attention based graph convolutional networks for aspect level sentiment classification"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "targeted sentiment classification with attentional encoder network"
    - "densely connected graph convolutional networks for graph to sequence learning"
    - "multi hop reading comprehension across multiple documents by reasoning over heterogeneous graphs"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "attention guided graph convolutional networks for relation extraction"
    - "modeling sentiment dependencies with graph convolutional networks for aspect level sentiment classification"
    - "exploiting coarse to fine task transfer for aspect level sentiment classification"

Connected through hyperedge HE-6 (induced by source paper "open domain targeted sentiment analysis via span based extraction and classification"):
  with members:
    - "parameterized convolutional neural networks for aspect level sentiment classification"
    - "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"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "end to end neural coreference resolution"

Connected through hyperedge HE-7 (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-8 (induced by source paper "you shall know a user by the company it keeps dynamic representations for social media users in nlp"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "abusive language detection with graph convolutional networks"
    - "representing social media users for sarcasm detection"
    - "large scale crowdsourcing and characterization of twitter abusive behavior"
    - "cascade contextual sarcasm detection in online discussion forums"
    - "assessing state of the art sentiment models on state of the art sentiment datasets"
    - "attention is all you need"
    - "a decomposable attention model for natural language inference"

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)