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: "erasmus efficient remote attestation via self measurement for unattended settings"
  Abstract: Remote attestation (RA) is a popular means of detecting malware in embedded and IoT devices. RA is usually realized as an interactive protocol, whereby a trusted party -- verifier -- measures integrity of a potentially compromised remote device -- prover. Early work focused on purely software-based and fully hardware-based techniques, neither of which is ideal for low-end devices. More recent results have yielded hybrid (SW/HW) security architectures comprised of a minimal set of features to support efficient and secure RA on low-end devices. #R##N#All prior RA techniques require on-demand operation, i.e, RA is performed in real time. We identify some drawbacks of this general approach in the context of unattended devices: First, it fails to detect mobile malware that enters and leaves the prover between successive RA instances. Second, it requires the prover to engage in a potentially expensive (in terms of time and energy) computation, which can be harmful for critical or real-time devices. #R##N#To address these drawbacks, we introduce the concept of self-measurement where a prover device periodically (and securely) measures and records its own software state, based on a pre-established schedule. A possibly untrusted verifier occasionally collects and verifies these measurements. We present the design of a concrete technique called ERASMUS : Efficient Remote Attestation via Self-Measurement for Unattended Settings, justify its features and evaluate its performance. In the process, we also define a new metric -- Quality of Attestation (QoA). We argue that ERASMUS is well-suited for time-sensitive and/or safety-critical applications that are not served well by on-demand RA. Finally, we show that ERASMUS is a promising stepping stone towards handling attestation of multiple devices (i.e., a group or swarm) with high mobility.

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)