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: "hadoop perfect file a fast access container for small files with direct in disc metadata access"
  Abstract: Storing and processing massive small files is one of the major challenges for the Hadoop Distributed File System (HDFS). In order to provide fast data access, the NameNode (NN) in HDFS maintains the metadata of all files in its main-memory. Hadoop performs well with a small number of large files that require relatively little metadata in the NN s memory. But for a large number of small files, Hadoop has problems such as NN memory overload caused by the huge metadata size of these small files. We present a new type of archive file, Hadoop Perfect File (HPF), to solve HDFS s small files problem by merging small files into a large file on HDFS. Existing archive files offer limited functionality and have poor performance when accessing a file in the merged file due to the fact that during metadata lookup it is necessary to read and process the entire index file(s). In contrast, HPF file can directly access the metadata of a particular file from its index file without having to process it entirely. The HPF index system uses two hash functions: file s metadata are distributed through index files by using a dynamic hash function and, for each index file, we build an order preserving perfect hash function that preserves the position of each file s metadata in the index file. The HPF design will only read the part of the index file that contains the metadata of the searched file during its access. HPF file also supports the file appending functionality after its creation. Our experiments show that HPF can be more than 40% faster file s access from the original HDFS. If we don t consider the caching effect, HPF s file access is around 179% faster than MapFile and 11294% faster than HAR file. If we consider caching effect, HPF is around 35% faster than MapFile and 105% faster than HAR file.

Connected through hyperedge HE-1 (induced by source paper "storage solutions for big data systems a qualitative study and comparison"):
  with members:
    - "a billion updates per second using 30 000 hierarchical in memory d4m databases"
    - "foundationdb record layer a multi tenant structured datastore"
    - "decentralized search on decentralized web"
    - "effects of storage heterogeneity in distributed cache systems"
    - "database as a service current issues and its future"
    - "chain of trust can trusted hardware help scaling blockchains"
    - "towards scaling blockchain systems via sharding"
    - "a stitch in time saves nine sparql querying of property graphs using gremlin traversals"

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.DC(Distributed, Parallel, and Cluster Computing)