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: "sequential short text classification with recurrent and convolutional neural networks"
  Abstract: Recent approaches based on artificial neural networks (ANNs) have shown promising results for short-text classification. However, many short texts occur in sequences (e.g., sentences in a document or utterances in a dialog), and most existing ANN-based systems do not leverage the preceding short texts when classifying a subsequent one. In this work, we present a model based on recurrent neural networks and convolutional neural networks that incorporates the preceding short texts. Our model achieves state-of-the-art results on three different datasets for dialog act prediction.

Connected through hyperedge HE-1 (induced by source paper "local contextual attention with hierarchical structure for dialogue act recognition"):
  with members:
    - "dialogue act classification with context aware self attention"
    - "self attention with relative position representations"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "a deep sequential model for discourse parsing on multi party dialogues"
    - "a context based approach for dialogue act recognition using simple recurrent neural networks"
    - "modeling localness for self attention networks"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "a post processing method for detecting unknown intent of dialogue system via pre trained deep neural network classifier"):
  with members:
    - "deep unknown intent detection with margin loss"
    - "a bi model based rnn semantic frame parsing model for intent detection and slot filling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint learning of domain classification and out of domain detection with dynamic class weighting for satisficing false acceptance rates"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "on calibration of modern neural networks"
    - "doc deep open classification of text documents"
    - "open category classification by adversarial sample generation"

Connected through hyperedge HE-3 (induced by source paper "cosql a conversational text to sql challenge towards cross domain natural language interfaces to databases"):
  with members:
    - "towards complex text to sql in cross domain database with intermediate representation"
    - "sparc cross domain semantic parsing in context"
    - "representing schema structure with graph neural networks for text to sql parsing"
    - "editing based sql query generation for cross domain context dependent questions"
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "learning to map context dependent sentences to executable formal queries"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sql to text generation with graph to sequence model"

Connected through hyperedge HE-4 (induced by source paper "collaborative multi agent dialogue model training via reinforcement learning"):
  with members:
    - "textworld a learning environment for text based games"
    - "switch based active deep dyna q efficient adaptive planning for task completion dialogue policy learning"
    - "neural approaches to conversational ai"
    - "counterfactual multi agent policy gradients"
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "end to end task completion neural dialogue systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "dialogue act classification with context aware self attention"):
  with members:
    - "multi level gated recurrent neural network for dialog act classification"
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "a dual attention hierarchical recurrent neural network for dialogue act classification"
    - "improved dynamic memory network for dialogue act classification with adversarial training"
    - "deep contextualized word representations"
    - "dialogue act recognition via crf attentive structured network"
    - "neural based context representation learning for dialog act classification"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "sequential learning of convolutional features for effective text classification"):
  with members:
    - "effectiveness of self normalizing neural networks for text classification"
    - "gated convolutional neural networks for domain adaptation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a structured self attentive sentence embedding"
    - "attentive pooling networks"
    - "very deep convolutional networks for text classification"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-7 (induced by source paper "energy based self attentive learning of abstractive communities for spoken language understanding"):
  with members:
    - "attention is all you need"
    - "exploiting cross sentence context for neural machine translation"
    - "get to the point summarization with pointer generator networks"
    - "a structured self attentive sentence embedding"
    - "modeling coverage for neural machine translation"
    - "effective approaches to attention based neural machine translation"
    - "facenet a unified embedding for face recognition and clustering"
    - "generating abstractive summaries from meeting transcripts"

Connected through hyperedge HE-8 (induced by source paper "short text conversation based on deep neural network and analysis on evaluation measures"):
  with members:
    - "a deep cascade model for multi document reading comprehension"
    - "multi style generative reading comprehension"
    - "know what you don t know unanswerable questions for squad"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "adversarial learning for neural dialogue generation"
    - "dialogue act sequence labeling using hierarchical encoder with crf"
    - "reinforced mnemonic reader for machine reading comprehension"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"

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)