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: "the transfer of human trust in robot capabilities across tasks"
  Abstract: Trust is crucial in shaping human interactions with one another and with robots. This work investigates how human trust in robot capabilities transfers across tasks. We present a human-subjects study of two distinct task domains: a Fetch robot performing household tasks and a virtual reality simulation of an autonomous vehicle performing driving and parking maneuvers. Our findings lead to a functional view of trust and two novel predictive models---a recurrent neural network architecture and a Bayesian Gaussian process---that capture trust evolution and transfer via latent task representations. Experiments show that the two proposed models outperform existing approaches when predicting trust across unseen tasks and participants. These results indicate that (i) a task-dependent functional trust model captures human trust in robot capabilities more accurately, and (ii) trust transfer across tasks can be inferred to a good degree. The latter enables trust-based robot decision-making for fluent human-robot interaction. In particular, our models can be used to derive robot policies that mitigate under-trust or over-trust by human teammates in collaborative multi-task settings.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning with fairness constraints for resource distribution in human robot teams"):
  with members:
    - "the assistive multi armed bandit"
    - "combinatorial sleeping bandits with fairness constraints"
    - "human ai learning performance in multi armed bandits"
    - "fairness in learning classic and contextual bandits"
    - "efficient optimal learning for contextual bandits"
    - "a contextual bandit approach to personalized news article recommendation"
    - "an axiomatic theory of fairness in network resource allocation"

Connected through hyperedge HE-2 (induced by source paper "trust aware decision making for human robot collaboration model learning and planning"):
  with members:
    - "shared autonomy via hindsight optimization"
    - "efficient model learning from joint action demonstrations for human robot collaborative tasks"

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.RO(Robotics)