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 in reinforcement learning agents and robots a survey"
  Abstract: This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores human user emotions. Emotions are recognized as functional in decision-making by influencing motivation and action selection. Therefore, computational emotion models are usually grounded in the agent’s decision making architecture, of which RL is an important subclass. Studying emotions in RL-based agents is useful for three research fields. For machine learning (ML) researchers, emotion models may improve learning efficiency. For the interactive ML and human–robot interaction community, emotions can communicate state and enhance user investment. Lastly, it allows affective modelling researchers to investigate their emotion theories in a successful AI agent class. This survey provides background on emotion theory and RL. It systematically addresses (1) from what underlying dimensions (e.g. homeostasis, appraisal) emotions can be derived and how these can be modelled in RL-agents, (2) what types of emotions have been derived from these dimensions, and (3) how these emotions may either influence the learning efficiency of the agent or be useful as social signals. We also systematically compare evaluation criteria, and draw connections to important RL sub-domains like (intrinsic) motivation and model-based RL. In short, this survey provides both a practical overview for engineers wanting to implement emotions in their RL agents, and identifies challenges and directions for future emotion-RL research.

Connected through hyperedge HE-1 (induced by source paper "natural language generation using reinforcement learning with external rewards"):
  with members:
    - "deep reinforcement learning for modeling chit chat dialog with discrete attributes"
    - "polite dialogue generation without parallel data"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "a survey on dialogue systems recent advances and new frontiers"
    - "affective neural response generation"
    - "q learning with basic emotions"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"
    - "a neural conversational model"

Connected through hyperedge HE-2 (induced by source paper "reinforcement learning based emotional editing constraint conversation generation"):
  with members:
    - "deep reinforcement learning for conversational ai"
    - "a survey on multi task learning"
    - "deep reinforcement learning for dialogue generation"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "deltableu a discriminative metric for generation tasks with intrinsically diverse targets"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-3 (induced by source paper "towards empathetic planning"):
  with members:
    - "back to the future for dialogue research a position paper"
    - "cooperative epistemic multi agent planning for implicit coordination"
    - "plan explanations as model reconciliation moving beyond explanation as soliloquy"
    - "the off switch game"

Connected through hyperedge HE-4 (induced by source paper "a generic framework for task selection driven by synthetic emotions"):
  with members:
    - "the starcraft multi agent challenge"
    - "multi task learning as multi objective optimization"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"

Connected through hyperedge HE-5 (induced by source paper "conservatives overfit liberals underfit the social psychological control of affect and uncertainty"):
  with members:
    - "affect control processes"

Connected through hyperedge HE-6 (induced by source paper "improving humanness of virtual agents and users cooperation through emotions"):
  with members:
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "a deep neural model of emotion appraisal"):
  with members:
    - "the omg emotion behavior dataset"
    - "a deep network for arousal valence emotion prediction with acoustic visual cues"
    - "multimodal emotion recognition for one minute gradual emotion challenge"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"
    - "examining representational similarity in convnets and the primate visual cortex"
    - "emonets multimodal deep learning approaches for emotion recognition in video"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "deep emotion a computational model of emotion using deep neural networks"):
  with members:
    - "continuous control with deep reinforcement learning"

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.LG(Machine Learning)