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: "a path to ai"
  Abstract: To build a safe system that would replicate and perhaps transcend human-level intelligence, a system composed of three basic modules: objective, agent, and perception is proposed for development. An objective module would make sure the system acts in humanity's interest, not against them and would have two components: a network of machine learning agents and a cryptographic ledger. The network of agents would benefit from a form of semiotics, known as deconstruction to address the problem of value alignment. A decentralized cryptographic ledger to propose a mechanism to mitigate the existential threat such a system would pose to humanity. To build the agent module that would act intelligently on the world, the concept of Dyna would benefit from a treatise in sociology to build up the theoretical framework needed for the missing link of artificial general intelligence - the world simulator. A perception module that would estimate the state of the world, would benefit from existing deep learning algorithms enhanced by a new paradigm in hardware design - a quantum computer. This paper describes a way of how such a system could be built, analyzing the current state of the art and providing rather alternative directions for research than concrete, industry-ready solutions.

Connected through hyperedge HE-1 (induced by source paper "spatial knowledge distillation to aid visual reasoning"):
  with members:
    - "attention is all you need"
    - "a simple neural network module for relational reasoning"
    - "learning to reason end to end module networks for visual question answering"
    - "visual relationship detection with internal and external linguistic knowledge distillation"
    - "film visual reasoning with a general conditioning layer"
    - "sequence level knowledge distillation"
    - "text guided attention model for image captioning"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"

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)