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 "efficient algorithms for densest subgraph discovery"):
  Abstract: Densest subgraph discovery (DSD) is a fundamental problem in graph mining. It has been studied for decades, and is widely used in various areas, including network science, biological analysis, and graph databases. Given a graph G, DSD aims to find a subgraph D of G with the highest density (e.g., the number of edges over the number of vertices in D). Because DSD is difficult to solve, we propose a new solution paradigm in this paper. Our main observation is that a densest subgraph can be accurately found through a k-core (a kind of dense subgraph of G), with theoretical guarantees. Based on this intuition, we develop efficient exact and approximation solutions for DSD. Moreover, our solutions are able to find the densest subgraphs for a wide range of graph density definitions, including clique-based and general pattern-based density. We have performed extensive experimental evaluation on eleven real datasets. Our results show that our algorithms are up to four orders of magnitude faster than existing approaches.

Includes members:
  Member "exploring communities in large profiled graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey of community search over big graphs")
  Member "a survey of community search over big graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "span core decomposition for temporal networks algorithms and applications")
  Member "local algorithms for hierarchical dense subgraph discovery":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "shared memory parallel maximal clique enumeration from static and dynamic graphs")
  Member "vertex priority based butterfly counting for large scale bipartite networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient bitruss decomposition for large scale bipartite graphs")
  Member "the ubiquity of large graphs and surprising challenges of graph processing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries")
  Member "density friendly graph decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "computing the k densest subgraphs of a graph")
  Member "path sampling a fast and provable method for estimating 4 vertex subgraph counts":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the power of pivoting for exact clique counting")
  Member "mathematical and algorithmic analysis of network and biological data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "joint alignment from pairwise differences with a noisy oracle")

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)