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: "affect lm a neural language model for customizable affective text generation"
  Abstract: Human verbal communication includes affective messages which are conveyed through use of emotionally colored words. There has been a lot of research in this direction but the problem of integrating state-of-the-art neural language models with affective information remains an area ripe for exploration. In this paper, we propose an extension to an LSTM (Long Short-Term Memory) language model for generating conversational text, conditioned on affect categories. Our proposed model, Affect-LM enables us to customize the degree of emotional content in generated sentences through an additional design parameter. Perception studies conducted using Amazon Mechanical Turk show that Affect-LM generates naturally looking emotional sentences without sacrificing grammatical correctness. Affect-LM also learns affect-discriminative word representations, and perplexity experiments show that additional affective information in conversational text can improve language model prediction.

Connected through hyperedge HE-1 (induced by source paper "generating emotionally aligned responses in dialogues using affect control theory"):
  with members:
    - "disentangled representation learning for non parallel text style transfer"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "emma an emotionally intelligent personal assistant for improving wellbeing"
    - "finding good representations of emotions for text classification"
    - "sentiment adaptive end to end dialog systems"
    - "toward controlled generation of text"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "mojitalk generating emotional responses at scale"

Connected through hyperedge HE-2 (induced by source paper "expression analysis based on face regions in read world conditions"):
  with members:
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "attention is all you need"
    - "dual path networks"
    - "affective neural response generation"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"
    - "from facial expression recognition to interpersonal relation prediction"
    - "densely connected convolutional networks"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"

Connected through hyperedge HE-3 (induced by source paper "a survey of natural language generation techniques with a focus on dialogue systems past present and future directions"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "personalized dialogue generation with diversified traits"
    - "learning to write with cooperative discriminators"
    - "training millions of personalized dialogue agents"
    - "towards empathetic open domain conversation models a new benchmark and dataset"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "polite dialogue generation without parallel data"

Connected through hyperedge HE-4 (induced by source paper "consistent dialogue generation with self supervised feature learning"):
  with members:
    - "jointly optimizing diversity and relevance in neural response generation"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "dialogue natural language inference"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "neural approaches to conversational ai"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "audio visual scene analysis with self supervised multisensory features"

Connected through hyperedge HE-5 (induced by source paper "a multi turn emotionally engaging dialog model"):
  with members:
    - "roberta a robustly optimized bert pretraining approach"
    - "towards empathetic open domain conversation models a new benchmark and dataset"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an affect rich neural conversational model with biased attention and weighted cross entropy loss"
    - "touch your heart a tone aware chatbot for customer care on social media"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "mojitalk generating emotional responses at scale"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "emotional neural language generation grounded in situational contexts"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "the curious case of neural text degeneration"
    - "emotion recognition in conversation research challenges datasets and recent advances"
    - "encoder agnostic adaptation for conditional language generation"
    - "towards best experiment design for evaluating dialogue system output"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "what does bert look at an analysis of bert s attention"
    - "personalizing dialogue agents i have a dog do you have pets too"

Connected through hyperedge HE-7 (induced by source paper "towards content transfer through grounded text generation"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "hierarchical neural story generation"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "style transfer from non parallel text by cross alignment"
    - "a nested attention neural hybrid model for grammatical error correction"

Connected through hyperedge HE-8 (induced by source paper "empgan multi resolution interactive empathetic dialogue generation"):
  with members:
    - "affect driven dialog generation"
    - "a syntactically constrained bidirectional asynchronous approach for emotional conversation generation"
    - "towards empathetic open domain conversation models a new benchmark and dataset"
    - "an affect rich neural conversational model with biased attention and weighted cross entropy loss"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "mojitalk generating emotional responses at scale"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "adversarial learning for neural dialogue generation"

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)