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: "reasoning about cognitive trust in stochastic multiagent systems"
  Abstract: We consider the setting of stochastic multiagent systems modelled as stochastic multiplayer games and formulate an automated verification framework for quantifying and reasoning about agents’ trust. To capture human trust, we work with a cognitive notion of trust defined as a subjective evaluation that agent A makes about agent B’s ability to complete a task, which in turn may lead to a decision by A to rely on B. We propose a probabilistic rational temporal logic PRTLa, which extends the probabilistic computation tree logic PCTLa with reasoning about mental attitudes (beliefs, goals, and intentions) and includes novel operators that can express concepts of social trust such as competence, disposition, and dependence. The logic can express, for example, that “agent A will eventually trust agent B with probability at least p that B will behave in a way that ensures the successful completion of a given task.” We study the complexity of the automated verification problem and, while the general problem is undecidable, we identify restrictions on the logic and the system that result in decidable, or even tractable, subproblems.

Connected through hyperedge HE-1 (induced by source paper "reliability validation of learning enabled vehicle tracking"):
  with members:
    - "test metrics for recurrent neural networks"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "detecting and tracking small moving objects in wide area motion imagery wami using convolutional neural networks cnns"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "symbolic execution for deep neural networks"
    - "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"

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.LO(Logic in Computer Science)