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: "the ubiquity of large graphs and surprising challenges of graph processing"
  Abstract: Graph processing is becoming increasingly prevalent across many application domains. In spite of this prevalence, there is little research about how graphs are actually used in practice. We conducted an online survey aimed at understanding: (i) the types of graphs users have; (ii) the graph computations users run; (iii) the types of graph software users use; and (iv) the major challenges users face when processing their graphs. We describe the participants' responses to our questions highlighting common patterns and challenges. We further reviewed user feedback in the mailing lists, bug reports, and feature requests in the source repositories of a large suite of software products for processing graphs. Through our review, we were able to answer some new questions that were raised by participants' responses and identify specific challenges that users face when using different classes of graph software. The participants' responses and data we obtained revealed surprising facts about graph processing in practice. In particular, real-world graphs represent a very diverse range of entities and are often very large, and scalability and visualization are undeniably the most pressing challenges faced by participants. We hope these findings can guide future research.

Connected through hyperedge HE-1 (induced by source paper "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"):
  with members:
    - "graph processing on fpgas taxonomy survey challenges"
    - "slim fly a cost effective low diameter network topology"
    - "fatpaths routing in supercomputers data centers and clouds with low diameter networks when shortest paths fall short"
    - "active access a mechanism for high performance distributed data centric computations"
    - "network accelerated non contiguous memory transfers"
    - "slim graph practical lossy graph compression for approximate graph processing storage and analytics"
    - "survey and taxonomy of lossless graph compression and space efficient graph representations"
    - "high level programming abstractions for distributed graph processing"

Connected through hyperedge HE-2 (induced by source paper "near optimal fully dynamic densest subgraph"):
  with members:
    - "distributed dense subgraph detection and low outdegree orientation"
    - "new algorithms and lower bounds for all pairs max flow in undirected graphs"
    - "inapproximability of maximum biclique problems minimum k cut and densest at least k subgraph from the small set expansion hypothesis"
    - "fully dynamic approximate maximum matching and minimum vertex cover in o log 3 n worst case update time"
    - "egalitarian graph orientations"
    - "new deterministic approximation algorithms for fully dynamic matching"
    - "popular conjectures as a barrier for dynamic planar graph algorithms"
    - "unifying and strengthening hardness for dynamic problems via the online matrix vector multiplication conjecture"

Connected through hyperedge HE-3 (induced by source paper "near optimal massively parallel graph connectivity"):
  with members:
    - "connected components on a pram in log diameter time"
    - "graph connectivity in log diameter steps using label propagation"
    - "matching and mis for uniformly sparse graphs in the low memory mpc model"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "approximating edit distance in truly subquadratic time quantum and mapreduce"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "connected components at scale via local contractions"
    - "massively parallel symmetry breaking on sparse graphs mis and maximal matching"

Connected through hyperedge HE-4 (induced by source paper "parallel batch dynamic graph connectivity"):
  with members:
    - "theoretically efficient parallel graph algorithms can be fast and scalable"
    - "parallel working set search structures"
    - "improved worst case deterministic parallel dynamic minimum spanning forest"
    - "pam parallel augmented maps"
    - "batch parallel euler tour trees"
    - "near optimal parallel algorithms for dynamic dfs in undirected graphs"
    - "fully dynamic connectivity in o log n log log n 2 amortized expected time"
    - "fully dynamic minimum spanning forest with improved worst case update time"

Connected through hyperedge HE-5 (induced by source paper "efficient algorithms for densest subgraph discovery"):
  with members:
    - "exploring communities in large profiled graphs"
    - "a survey of community search over big graphs"
    - "local algorithms for hierarchical dense subgraph discovery"
    - "vertex priority based butterfly counting for large scale bipartite networks"
    - "density friendly graph decomposition"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "mathematical and algorithmic analysis of network and biological data"
    - "finding the hierarchy of dense subgraphs using nucleus decompositions"

Connected through hyperedge HE-6 (induced by source paper "the triejax architecture accelerating graph operations through relational joins"):
  with members:
    - "size bounds and query plans for relational joins"
    - "flexible caching in trie joins"
    - "join processing for graph patterns an old dog with new tricks"
    - "faq questions asked frequently"
    - "beyond worst case analysis for joins with minesweeper"
    - "skew strikes back new developments in the theory of join algorithms"

Connected through hyperedge HE-7 (induced by source paper "distributed graphs in search of fast low latency resource efficient semantics rich big data processing"):
  with members:
    - "high level programming abstractions for distributed graph processing"
    - "ringo interactive graph analytics on big memory machines"

Connected through hyperedge HE-8 (induced by source paper "approximate evaluation of label constrained reachability queries"):
  with members:
    - "estimating the cardinality of conjunctive queries over rdf data using graph summarisation"
    - "gmark schema driven generation of graphs and queries"
    - "g core a core for future graph query languages"
    - "an analytical study of large sparql query logs"
    - "foundations of modern query languages for graph databases"
    - "graph summarization methods and applications a survey"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

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)