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: "deep memory networks for attitude identification"
  Abstract: We consider the task of identifying attitudes towards a given set of entities from text. Conventionally, this task is decomposed into two separate subtasks: target detection that identifies whether each entity is mentioned in the text, either explicitly or implicitly, and polarity classification that classifies the exact sentiment towards an identified entity (the target) into positive, negative, or neutral.   Instead, we show that attitude identification can be solved with an end-to-end machine learning architecture, in which the two subtasks are interleaved by a deep memory network. In this way, signals produced in target detection provide clues for polarity classification, and reversely, the predicted polarity provides feedback to the identification of targets. Moreover, the treatments for the set of targets also influence each other -- the learned representations may share the same semantics for some targets but vary for others. The proposed deep memory network, the AttNet, outperforms methods that do not consider the interactions between the subtasks or those among the targets, including conventional machine learning methods and the state-of-the-art deep learning models.

Connected through hyperedge HE-1 (induced by source paper "dan dual view representation learning for adapting stance classifiers to new domains"):
  with members:
    - "recognising agreement and disagreement between stances with reason comparing networks"
    - "domain adaptation with adversarial training and graph embeddings"
    - "aspect augmented adversarial networks for domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "stance detection with bidirectional conditional encoding"
    - "deep coral correlation alignment for deep domain adaptation"
    - "bag of tricks for efficient text classification"
    - "mitre at semeval 2016 task 6 transfer learning for stance detection"

Connected through hyperedge HE-2 (induced by source paper "modeling sentiment dependencies with graph convolutional networks for aspect level sentiment classification"):
  with members:
    - "multi label image recognition with graph convolutional networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "sentence state lstm for text representation"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "interactive attention networks for aspect level sentiment classification"
    - "a deeper look into sarcastic tweets using deep convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "deep learning for sentiment analysis a survey"):
  with members:
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "tensor fusion network for multimodal sentiment analysis"
    - "sarcasm sign interpreting sarcasm with sentiment based monolingual machine translation"
    - "volatility prediction using financial disclosures sentiments with word embedding based ir models"
    - "interactive attention networks for aspect level sentiment classification"
    - "recursive neural conditional random fields for aspect based sentiment analysis"
    - "a deeper look into sarcastic tweets using deep convolutional neural networks"
    - "a hierarchical model of reviews for aspect based sentiment analysis"

Connected through hyperedge HE-4 (induced by source paper "collaborative memory network for recommendation systems"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "neural collaborative filtering"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "autosvd an efficient hybrid collaborative filtering model via contractive auto encoders"
    - "attentional factorization machines learning the weight of feature interactions via attention networks"
    - "wide deep learning for recommender systems"
    - "dynamic memory networks for visual and textual question answering"
    - "end to end memory networks"

Connected through hyperedge HE-5 (induced by source paper "positivity bias in customer satisfaction ratings"):
  with members:
    - "large scale analysis of counseling conversations an application of natural language processing to mental health"
    - "mining the minds of customers from online chat logs"
    - "effective approaches to attention based neural machine translation"
    - "improved semantic representations from tree structured long short term memory networks"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "convolutional neural networks for sentence 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)