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 control processes"
  Abstract: This paper describes a novel method for building affectively intelligent human-interactive agents. The method is based on a key sociological insight that has been developed and extensively verified over the last twenty years, but has yet to make an impact in artificial intelligence. The insight is that resource bounded humans will, by default, act to maintain affective consistency. Humans have culturally shared fundamental affective sentiments about identities, behaviours, and objects, and they act so that the transient affective sentiments created during interactions confirm the fundamental sentiments. Humans seek and create situations that confirm or are consistent with, and avoid and suppress situations that disconfirm or are inconsistent with, their culturally shared affective sentiments. This "affect control principle" has been shown to be a powerful predictor of human behaviour. In this paper, we present a probabilistic and decision-theoretic generalisation of this principle, and we demonstrate how it can be leveraged to build affectively intelligent artificial agents. The new model, called BayesAct, can maintain multiple hypotheses about sentiments simultaneously as a probability distribution, and can make use of an explicit utility function to make value-directed action choices. This allows the model to generate affectively intelligent interactions with people by learning about their identity, predicting their behaviours using the affect control principle, and taking actions that are simultaneously goal-directed and affect-sensitive. We demonstrate this generalisation with a set of simulations. We then show how our model can be used as an emotional "plug-in" for artificially intelligent systems that interact with humans in two different settings: an exam practice assistant (tutor) and an assistive device for persons with a cognitive disability.

Connected through hyperedge HE-1 (induced by source paper "multi agent reinforcement learning and human social factors in climate change mitigation"):
  with members:
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "cooperating with machines"
    - "prosocial learning agents solve generalized stag hunts better than selfish ones"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "stance and sentiment in tweets"
    - "multiagent cooperation and competition with deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "conservatives overfit liberals underfit the social psychological control of affect and uncertainty"):
  with members:
    - "emotion in reinforcement learning agents and robots a survey"

Connected through hyperedge HE-3 (induced by source paper "affective neural response generation"):
  with members:
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "latent intention dialogue models"
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "latent variable dialogue models and their diversity"
    - "adversarial learning for neural dialogue generation"
    - "affect lm a neural language model for customizable affective text generation"
    - "deep reinforcement learning for dialogue generation"
    - "a simple fast diverse decoding algorithm for neural generation"

Connected through hyperedge HE-4 (induced by source paper "online sequence to sequence active learning for open domain dialogue generation"):
  with members:
    - "key value retrieval networks for task oriented dialogue"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "agent agnostic human in the loop reinforcement learning"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "end to end task completion neural dialogue systems"
    - "a conditional variational framework for dialog generation"
    - "a copy augmented sequence to sequence architecture gives good performance on task oriented dialogue"
    - "investigation of language understanding impact for reinforcement learning based dialogue systems"

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.HC(Human-Computer Interaction)