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: "accelerating analytical processing in mvcc using fine granular high frequency virtual snapshotting"
  Abstract: Efficient transactional management is a delicate task. As systems face transactions of inherently different types, ranging from point updates to long running analytical computations, it is hard to satisfy their individual requirements with a single processing component. Unfortunately, most systems nowadays rely on such a single component that implements its parallelism using multi-version concurrency control (MVCC). While MVCC parallelizes short-running OLTP transactions very well, it struggles in the presence of mixed workloads containing long-running scan-centric OLAP queries, as scans have to work their way through large amounts of versioned data. To overcome this problem, we propose a system, which reintroduces the concept of heterogeneous transaction processing: OLAP transactions are outsourced to run on separate (virtual) snapshots while OLTP transactions run on the most recent representation of the database. Inside both components, MVCC ensures a high degree of concurrency. The biggest challenge of such a heterogeneous approach is to generate the snapshots at a high frequency. Previous approaches heavily suffered from the tremendous cost of snapshot creation. In our system, we overcome the restrictions of the OS by introducing a custom system call vm_snapshot, that is hand-tailored to our precise needs: it allows fine-granular snapshot creation at very high frequencies, rendering the snapshot creation phase orders of magnitudes faster than state-of-the-art approaches. Our experimental evaluation on a heterogeneous workload based on TPC-H transactions and handcrafted OLTP transactions shows that our system enables significantly higher analytical transaction throughputs on mixed workloads than homogeneous approaches. In this sense, we introduce a system that accelerates Analytical processing by introducing custom Kernel functionalities: AnKerDB.

Connected through hyperedge HE-1 (induced by source paper "chainifydb how to blockchainify any data management system"):
  with members:
    - "blockchain meets database design and implementation of a blockchain relational database"
    - "hyperledger fabric a distributed operating system for permissioned blockchains"
    - "towards scaling blockchain systems via sharding"
    - "logbase a scalable log structured database system in the cloud"

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)