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: "interactive analytical processing in big data systems a cross industry study of mapreduce workloads"
  Abstract: Within the past few years, organizations in diverse industries have adopted MapReduce-based systems for large-scale data processing. Along with these new users, important new workloads have emerged which feature many small, short, and increasingly interactive jobs in addition to the large, long-running batch jobs for which MapReduce was originally designed. As interactive, large-scale query processing is a strength of the RDBMS community, it is important that lessons from that field be carried over and applied where possible in this new domain. However, these new workloads have not yet been described in the literature. We fill this gap with an empirical analysis of MapReduce traces from six separate business-critical deployments inside Facebook and at Cloudera customers in e-commerce, telecommunications, media, and retail. Our key contribution is a characterization of new MapReduce workloads which are driven in part by interactive analysis, and which make heavy use of query-like programming frameworks on top of MapReduce. These workloads display diverse behaviors which invalidate prior assumptions about MapReduce such as uniform data access, regular diurnal patterns, and prevalence of large jobs. A secondary contribution is a first step towards creating a TPC-like data processing benchmark for MapReduce.

Connected through hyperedge HE-1 (induced by source paper "vat asymptotic cost analysis for multi level key value stores"):
  with members:
    - "basic performance measurements of the intel optane dc persistent memory module"

Connected through hyperedge HE-2 (induced by source paper "automating distributed tiered storage management in cluster computing"):
  with members:
    - "competitive caching with machine learned advice"
    - "reca an efficient reconfigurable cache architecture for storage systems with online workload characterization"
    - "a unified approach to interpreting model predictions"
    - "xgboost a scalable tree boosting system"
    - "why should i trust you explaining the predictions of any classifier"
    - "compacting transactional data in hybrid oltp olap databases"
    - "competitive paging algorithms"

Connected through hyperedge HE-3 (induced by source paper "load balancing performance in distributed storage with regular balanced redundancy"):
  with members:
    - "redundancy scheduling in systems with bi modal job service time distribution"
    - "service rate region of content access from erasure coded storage"
    - "on the service capacity region of accessing erasure coded content"
    - "optimal combinatorial batch codes based on block designs"
    - "memory allocation in distributed storage networks"
    - "balanced allocation on graphs"

Connected through hyperedge HE-4 (induced by source paper "scheduling with inexact job sizes the merits of shortest processing time first"):
  with members:
    - "revisiting size based scheduling with estimated job sizes"
    - "psbs practical size based scheduling"
    - "a simulator for data intensive job scheduling"

Connected through hyperedge HE-5 (induced by source paper "differential approximation and sprinting for multi priority big data engines"):
  with members:
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-6 (induced by source paper "pangea monolithic distributed storage for data analytics"):
  with members:
    - "plinycompute a platform for high performance distributed data intensive tool development"
    - "lifetime based memory management for distributed data processing systems"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "efficient analysis of caching strategies under dynamic content popularity"
    - "xoring elephants novel erasure codes for big data"
    - "the k server dual and loose competitiveness for paging"

Connected through hyperedge HE-7 (induced by source paper "trevor automatic configuration and scaling of stream processing pipelines"):
  with members:
    - "hemingway modeling distributed optimization algorithms"
    - "drs dynamic resource scheduling for real time analytics over fast streams"

Connected through hyperedge HE-8 (induced by source paper "intermediate data caching optimization for multi stage and parallel big data frameworks"):
  with members:
    - "adaptive caching networks with optimality guarantees"
    - "deep autoregressive networks"

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)