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: "automatic partitioning of database applications"
  Abstract: Database-backed applications are nearly ubiquitous in our daily lives. Applications that make many small accesses to the database create two challenges for developers: increased latency and wasted resources from numerous network round trips. A well-known technique to improve transactional database application performance is to convert part of the application into stored procedures that are executed on the database server. Unfortunately, this conversion is often difficult. In this paper we describe Pyxis, a system that takes database-backed applications and automatically partitions their code into two pieces, one of which is executed on the application server and the other on the database server. Pyxis profiles the application and server loads, statically analyzes the code's dependencies, and produces a partitioning that minimizes the number of control transfers as well as the amount of data sent during each transfer. Our experiments using TPC-C and TPC-W show that Pyxis is able to generate partitions with up to 3x reduction in latency and 1.7x improvement in throughput when compared to a traditional non-partitioned implementation and has comparable performance to that of a custom stored procedure implementation.

Connected through hyperedge HE-1 (induced by source paper "scheduling oltp transactions via machine learning"):
  with members:
    - "improving high contention oltp performance via transaction scheduling"
    - "design principles for scaling multi core oltp under high contention"
    - "high performance concurrency control mechanisms for main memory databases"

Connected through hyperedge HE-2 (induced by source paper "compiling database application programs"):
  with members:
    - "building efficient query engines in a high level language"
    - "dbtoaster higher order delta processing for dynamic frequently fresh views"

Connected through hyperedge HE-3 (induced by source paper "runtime optimization of join location in parallel data management systems"):
  with members:
    - "program transformations for asynchronous and batched query submission"
    - "cascading map side joins over hbase for scalable join processing"
    - "muppet mapreduce style processing of fast data"

Connected through hyperedge HE-4 (induced by source paper "building efficient query engines in a high level language"):
  with members:
    - "hpat high performance analytics with scripting ease of use"
    - "tupleware redefining modern analytics"
    - "the homeostasis protocol avoiding transaction coordination through program analysis"
    - "dbtoaster higher order delta processing for dynamic frequently fresh views"

Connected through hyperedge HE-5 (induced by source paper "parallel database recovery for multicore main memory databases"):
  with members:
    - "implementing performance competitive logical recovery"

Connected through hyperedge HE-6 (induced by source paper "fast failure recovery for main memory dbmss on multicores"):
  with members:
    - "implementing performance competitive logical recovery"

Connected through hyperedge HE-7 (induced by source paper "design principles for scaling multi core oltp under high contention"):
  with members:
    - "the homeostasis protocol avoiding transaction coordination through program analysis"
    - "rethinking serializable multiversion concurrency control"
    - "oltp on hardware islands"
    - "high performance concurrency control mechanisms for main memory databases"
    - "flat combining and the synchronization parallelism tradeoff"

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)