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: "emotion recognition in conversation research challenges datasets and recent advances"
  Abstract: Emotion is intrinsic to humans and consequently emotion understanding is a key part of human-like artificial intelligence (AI). Emotion recognition in conversation (ERC) is becoming increasingly popular as a new research frontier in natural language processing (NLP) due to its ability to mine opinions from the plethora of publicly available conversational data in platforms such as Facebook, Youtube, Reddit, Twitter, and others. Moreover, it has potential applications in health-care systems (as a tool for psychological analysis), education (understanding student frustration) and more. Additionally, ERC is also extremely important for generating emotion-aware dialogues that require an understanding of the user's emotions. Catering to these needs calls for effective and scalable conversational emotion-recognition algorithms. However, it is a strenuous problem to solve because of several research challenges. In this paper, we discuss these challenges and shed light on the recent research in this field. We also describe the drawbacks of these approaches and discuss the reasons why they fail to successfully overcome the research challenges in ERC.

Connected through hyperedge HE-1 (induced by source paper "knowledge enriched transformer for emotion detection in textual conversations"):
  with members:
    - "text generation from knowledge graphs with graph transformers"
    - "global to local memory pointer networks for task oriented dialogue"
    - "ntuer at semeval 2019 task 3 emotion classification with word and sentence representations in rcnn"
    - "transformer xl attentive language models beyond a fixed length context"
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "improving the transformer translation model with document level context"
    - "emotion detection in text a review"

Connected through hyperedge HE-2 (induced by source paper "emotion recognition in conversations with transfer learning from generative conversation modeling"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "transfer learning for sequence labeling using source model and target data"
    - "knowledge enriched transformer for emotion detection in textual conversations"
    - "higru hierarchical gated recurrent units for utterance level emotion recognition"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "dialoguegcn a graph convolutional neural network for emotion recognition in conversation"
    - "transfer learning for context aware question matching in information seeking conversations in e commerce"

Connected through hyperedge HE-3 (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"
    - "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"
    - "polite dialogue generation without parallel data"

Connected through hyperedge HE-4 (induced by source paper "dialoguegcn a graph convolutional neural network for emotion recognition in conversation"):
  with members:
    - "ana at semeval 2019 task 3 contextual emotion detection in conversations through hierarchical lstms and bert"
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "emotionlines an emotion corpus of multi party conversations"
    - "decision support with text based emotion recognition deep learning for affective computing"
    - "memory fusion network for multi view sequential learning"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "neural message passing for quantum chemistry"
    - "quasi recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "towards multimodal sarcasm detection an _obviously_ perfect paper"):
  with members:
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "representing social media users for sarcasm detection"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dialoguernn an attentive rnn for emotion detection in conversations"
    - "cascade contextual sarcasm detection in online discussion forums"
    - "tensor fusion network for multimodal sentiment analysis"
    - "harnessing cognitive features for sarcasm detection"

Connected through hyperedge HE-6 (induced by source paper "real time emotion recognition via attention gated hierarchical memory network"):
  with members:
    - "higru hierarchical gated recurrent units for utterance level emotion recognition"
    - "dialoguegcn a graph convolutional neural network for emotion recognition in conversation"
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "dialoguernn an attentive rnn for emotion detection in conversations"
    - "end to end memory networks"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-7 (induced by source paper "meld a multimodal multi party dataset for emotion recognition in conversations"):
  with members:
    - "multimodal sentiment analysis using hierarchical fusion with context modeling"
    - "emotionlines an emotion corpus of multi party conversations"
    - "towards empathetic open domain conversation models a new benchmark and dataset"
    - "dialoguernn an attentive rnn for emotion detection in conversations"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "tensor fusion network for multimodal sentiment analysis"
    - "a persona based neural conversation model"
    - "convolutional experts constrained local model for facial landmark detection"

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)