human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "evidence for the size principle in semantic and perceptual domains"):
  Abstract: Shepard's Universal Law of Generalization offered a compelling case for the first physics-like law in cognitive science that should hold for all intelligent agents in the universe. Shepard's account is based on a rational Bayesian model of generalization, providing an answer to the question of why such a law should emerge. Extending this account to explain how humans use multiple examples to make better generalizations requires an additional assumption, called the size principle: hypotheses that pick out fewer objects should make a larger contribution to generalization. The degree to which this principle warrants similarly law-like status is far from conclusive. Typically, evaluating this principle has not been straightforward, requiring additional assumptions. We present a new method for evaluating the size principle that is more direct, and apply this method to a diverse array of datasets. Our results provide support for the broad applicability of the size principle.

Includes members:
  Member "adapting deep network features to capture psychological representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "high level perceptual similarity is enabled by learning diverse tasks")
  Member "adjustable bounded rectifiers towards deep binary representations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "structured convolution matrices for energy efficient deep learning")

Question: Please tell me which class the source paper 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.AI(Artificial Intelligence)