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 blockchain based liability attribution framework for autonomous vehicles"
  Abstract: The advent of autonomous vehicles is envisaged to disrupt the auto insurance liability model.Compared to the the current model where liability is largely attributed to the driver,autonomous vehicles necessitate the consideration of other entities in the automotive ecosystem including the auto manufacturer,software provider,service technician and the vehicle owner.The proliferation of sensors and connecting technologies in autonomous vehicles enables an autonomous vehicle to gather sufficient data for liability attribution,yet increased connectivity exposes the vehicle to attacks from interacting entities.These possibilities motivate potential liable entities to repudiate their involvement in a collision event to evade liability. While the data collected from vehicular sensors and vehicular communications is an integral part of the evidence for arbitrating liability in the event of an accident,there is also a need to record all interactions between the aforementioned entities to identify potential instances of negligence that may have played a role in the accident.In this paper,we propose a BlockChain(BC) based framework that integrates the concerned entities in the liability model and provides untampered evidence for liability attribution and adjudication.We first describe the liability attribution model, identify key requirements and describe the adversarial capabilities of entities. Also,we present a detailed description of data contributing to evidence.Our framework uses permissioned BC and partitions the BC to tailor data access to relevant BC participants.Finally,we conduct a security analysis to verify that the identified requirements are met and resilience of our proposed framework to identified attacks.

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.CR(Cryptography and Security)