human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "general purpose join algorithms for listing triangles in large graphs"):
  Abstract: We investigate applying general-purpose join algorithms to the triangle listing problem in an out-of-core context. In particular, we focus on Leapfrog Triejoin (LFTJ) by Veldhuizen 2014, a recently proposed, worst-case optimal algorithm. We present "boxing": a novel, yet conceptually simple, approach for feeding input data to LFTJ. Our extensive analysis shows that this approach is I/O efficient, being worst-case optimal (in a certain sense). Furthermore, if input data is only a constant factor larger than the available memory, then a boxed LFTJ essentially maintains the CPU data-complexity of the vanilla LFTJ. Next, focusing on LFTJ applied to the triangle query, we show that for many graphs boxed LFTJ matches the I/O complexity of the recently by Hu, Tao and Yufei proposed specialized algorithm MGT for listing tiangles in an out-of-core setting. We also strengthen the analysis of LFTJ's computational complexity for the triangle query by considering families of input graphs that are characterized not only by the number of edges but also by a measure of their density. E.g., we show that LFTJ achieves a CPU complexity of O(|E|log|E|) for planar graphs, while on general graphs, no algorithm can be faster than O(|E|^{1.5}). Finally, we perform an experimental evaluation for the triangle listing problem confirming our theoretical results and showing the overall effectiveness of our approach. On all our real-world and synthetic data sets (some of which containing more than 1.2 billion edges) LFTJ in single-threaded mode is within a factor of 3 of the specialized MGT; a penalty that---as we demonstrate---can be alleviated by parallelization.

Includes members:
  Member "size bounds and query plans for relational joins":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "complexity analysis of general and fractional hypertree decompositions")
  Member "enumerating subgraph instances using map reduce":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey of community search over big graphs")

Question: Please tell me which class the source paper 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)