human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "dialoguegcn a graph convolutional neural network for emotion recognition in conversation"):
  Abstract: Emotion recognition in conversation (ERC) has received much attention, lately, from researchers due to its potential widespread applications in diverse areas, such as health-care, education, and human resources. In this paper, we present Dialogue Graph Convolutional Network (DialogueGCN), a graph neural network based approach to ERC. We leverage self and inter-speaker dependency of the interlocutors to model conversational context for emotion recognition. Through the graph network, DialogueGCN addresses context propagation issues present in the current RNN-based methods. We empirically show that this method alleviates such issues, while outperforming the current state of the art on a number of benchmark emotion classification datasets.

Includes members:
  Member "emotion recognition in conversation research challenges datasets and recent advances":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "knowledge enriched transformer for emotion detection in textual conversations")
  Member "ana at semeval 2019 task 3 contextual emotion detection in conversations through hierarchical lstms and bert":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bertscore evaluating text generation with bert")
  Member "meld a multimodal multi party dataset for emotion recognition in conversations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "knowledge enriched transformer for emotion detection in textual conversations")
  Member "emotionlines an emotion corpus of multi party conversations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "eda enriching emotional dialogue acts using an ensemble of neural annotators")
  Member "decision support with text based emotion recognition deep learning for affective computing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "emotion recognition in conversation research challenges datasets and recent advances")
  Member "memory fusion network for multi view sequential learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "emotions don t lie a deepfake detection method using audio visual affective cues")
  Member "emotional chatting machine emotional conversation generation with internal and external memory":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an empirical investigation of pre trained transformer language models for open domain dialogue generation")
  Member "neural message passing for quantum chemistry":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")

Question: Please tell me which class the source paper 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)