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: "flash based extended cache for higher throughput and faster recovery"
  Abstract: Considering the current price gap between disk and flash memory drives, for applications dealing with large scale data, it will be economically more sensible to use flash memory drives to supplement disk drives rather than to replace them. This paper presents FaCE, which is a new low-overhead caching strategy that uses flash memory as an extension to the DRAM buffer. FaCE aims at improving the transaction throughput as well as shortening the recovery time from a system failure. To achieve the goals, we propose two novel algorithms for flash cache management, namely, Multi-Version FIFO replacement and Group Second Chance. One striking result from FaCE is that using a small flash memory drive as a caching device could deliver even higher throughput than using a large flash memory drive to store the entire database tables. This was possible due to flash write optimization as well as disk access reduction obtained by the FaCE caching methods. In addition, FaCE takes advantage of the non-volatility of flash memory to fully support database recovery by extending the scope of a persistent database to include the data pages stored in the flash cache. We have implemented FaCE in the PostgreSQL open source database server and demonstrated its effectiveness for TPC-C benchmarks.

Connected through hyperedge HE-1 (induced by source paper "cache placement in two tier hetnets with limited storage capacity cache or buffer"):
  with members:
    - "optimal fairness aware time and power allocation in wireless powered communication networks"
    - "energy efficient resource allocation in machine to machine communications with multiple access and energy harvesting for iot"
    - "cooperative edge caching in user centric clustered mobile networks"
    - "association and load optimization with user priorities in load coupled heterogeneous networks"
    - "on consideration of content preference and sharing willingness in d2d assisted offloading"
    - "stochastic content centric multicast scheduling for cache enabled heterogeneous cellular networks"
    - "caching at the wireless edge design aspects challenges and future directions"
    - "wireless caching technical misconceptions and business barriers"

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.DB(Databases)