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: "using machine learning to guide cognitive modeling a case study in moral reasoning"
  Abstract: Large-scale behavioral datasets enable researchers to use complex machine learning algorithms to better predict human behavior, yet this increased predictive power does not always lead to a better understanding of the behavior in question. In this paper, we outline a data-driven, iterative procedure that allows cognitive scientists to use machine learning to generate models that are both interpretable and accurate. We demonstrate this method in the domain of moral decision-making, where standard experimental approaches often identify relevant principles that influence human judgments, but fail to generalize these findings to "real world" situations that place these principles in conflict. The recently released Moral Machine dataset allows us to build a powerful model that can predict the outcomes of these conflicts while remaining simple enough to explain the basis behind human decisions.

Connected through hyperedge HE-1 (induced by source paper "advances in machine learning for the behavioral sciences"):
  with members:
    - "wordnet feelings a linguistic categorisation of human feelings"
    - "mlic a maxsat based framework for learning interpretable classification rules"
    - "the interpretation of dream meaning resolving ambiguity using latent semantic analysis in a small corpus of text"
    - "semantics derived automatically from language corpora contain human like biases"
    - "on the latent variable interpretation in sum product networks"
    - "why should i trust you explaining the predictions of any classifier"
    - "deep learning in neural networks"
    - "parliamentary voting procedures agenda control manipulation and uncertainty"

Connected through hyperedge HE-2 (induced by source paper "scaling up psychology via scientific regret minimization a case study in moral decision making"):
  with members:
    - "flipout efficient pseudo independent weight perturbations on mini batches"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "a computational model of commonsense moral decision making"
    - "on calibration of modern neural networks"
    - "the theory is predictive but is it complete an application to human perception of randomness"
    - "evaluating and improving the correspondence between deep neural networks and human representations"
    - "how deep is knowledge tracing"

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.CY(Computers and Society)