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: "topic memory networks for short text classification"
  Abstract: Many classification models work poorly on short texts due to data sparsity. To address this issue, we propose topic memory networks for short text classification with a novel topic memory mechanism to encode latent topic representations indicative of class labels. Different from most prior work that focuses on extending features with external knowledge or pre-trained topics, our model jointly explores topic inference and text classification with memory networks in an end-to-end manner. Experimental results on four benchmark datasets show that our model outperforms state-of-the-art models on short text classification, meanwhile generates coherent topics.

Connected through hyperedge HE-1 (induced by source paper "automating app review response generation"):
  with members:
    - "what you say and how you say it joint modeling of topics and discourse in microblog conversations"
    - "a benchmark study on sentiment analysis for software engineering research"
    - "four commentaries on the use of students and professionals in empirical software engineering experiments"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "essmart way to manage user requests"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "automatically generating commit messages from diffs using neural machine translation"
    - "steering output style and topic in neural response generation"

Connected through hyperedge HE-2 (induced by source paper "what changed your mind the roles of dynamic topics and discourse in argumentation process"):
  with members:
    - "unsupervised dialog structure learning"
    - "what you say and how you say it joint modeling of topics and discourse in microblog conversations"
    - "what gets echoed understanding the pointers in explanations of persuasive arguments"
    - "a joint model of conversational discourse and latent topics on microblogs"
    - "attentive interaction model modeling changes in view in argumentation"
    - "joint modeling of content and discourse relations in dialogues"
    - "winning on the merits the joint effects of content and style on debate outcomes"
    - "argument strength is in the eye of the beholder audience effects in persuasion"

Connected through hyperedge HE-3 (induced by source paper "text classification with lexicon from preattention mechanism"):
  with members:
    - "attention please a critical review of neural attention models in natural language processing"
    - "no training required exploring random encoders for sentence classification"
    - "lifelong learning for sentiment classification"
    - "ncrf an open source neural sequence labeling toolkit"
    - "deriving machine attention from human rationales"
    - "a structured self attentive sentence embedding"
    - "dependency sensitive convolutional neural networks for modeling sentences and documents"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-4 (induced by source paper "key phrase classification in complex assignments"):
  with members:
    - "an empirical evaluation of imbalanced data strategies from a practitioner s point of view"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "why should i trust you explaining the predictions of any classifier"
    - "tuned models of peer assessment in moocs"

Connected through hyperedge HE-5 (induced by source paper "what you say and how you say it joint modeling of topics and discourse in microblog conversations"):
  with members:
    - "improving topic models with latent feature word representations"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "a joint model of conversational discourse and latent topics on microblogs"
    - "joint modeling of content and discourse relations in dialogues"
    - "find the conversation killers a predictive study of thread ending posts"
    - "discovering discrete latent topics with neural variational inference"
    - "jointly learning word embeddings and latent topics"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"

Connected through hyperedge HE-6 (induced by source paper "topic aware neural keyphrase generation for social media language"):
  with members:
    - "an integrated approach for keyphrase generation via exploring the power of retrieval and extraction"
    - "microblog hashtag generation via encoding conversation contexts"
    - "what you say and how you say it joint modeling of topics and discourse in microblog conversations"
    - "keyphrase generation with correlation constraints"
    - "a joint model of conversational discourse and latent topics on microblogs"
    - "title guided encoding for keyphrase generation"
    - "deep keyphrase generation"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-7 (induced by source paper "an introductory survey on attention mechanisms in nlp problems"):
  with members:
    - "self attention with relative position representations"
    - "dynamic meta embeddings for improved sentence representations"
    - "mars memory attention aware recommender system"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multi level structured self attentions for distantly supervised relation extraction"
    - "structured attention networks"
    - "a nested attention neural hybrid model for grammatical error correction"
    - "attention is all you need"

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)