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: "inverse modeling of complex interactive behavior with abc"
  Abstract: Can one make deep inferences about a user based only on observations of how she interacts? This paper contributes a methodology for inverse modeling in HCI, where the goal is to estimate a cognitive model from limited behavioral data. Given substantial diversity in users' intentions, strategies and abilities, this is a difficult problem and previously unaddressed in HCI. We show advances following an approach that combines (1) computational rationality, to predict how a user adapts to a task when her capabilities are known, and (2) approximate Bayesian computation (ABC) to estimate those capabilities. The benefit is that model parameters are conditioned on both prior knowledge and observations, which improves model validity and helps identify causes for observations. We demonstrate these benefits in a case of menu interaction where the method obtained accurate estimates of users' behavioral and cognitive features from selection time data only. Inverse modeling methods can advance theoretical HCI by bringing complex behavior within reach of modeling.

Connected through hyperedge HE-1 (induced by source paper "inverse reinforcement learning from summary data"):
  with members:
    - "inferring cognitive models from data using approximate bayesian computation"
    - "inverse reinforcement learning with simultaneous estimation of rewards and dynamics"
    - "apprenticeship learning using inverse reinforcement learning and gradient methods"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical 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.HC(Human-Computer Interaction)