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 "approximate computation for big data analytics"):
  Abstract: Over the past a few years, research and development has made significant progresses on big data analytics. A fundamental issue for big data analytics is the efficiency. If the optimal solution is unable to attain or not required or has a price to high to pay, it is reasonable to sacrifice optimality with a `good' feasible solution that can be computed efficiently. Existing approximation techniques can be in general classified into approximation algorithms, approximate query processing for aggregate SQL queries and approximation computing for multiple layers of the system stack. In this article, we systematically introduce approximate computation, i.e., query approximation and data approximation, for efficiency and effectiveness big data analytics. We first explain the idea and rationale of query approximation, and show efficiency can be obtained with high effectiveness in practice with three analytic tasks: graph pattern matching, trajectory compression and dense subgraph computation. We then explain the idea and rationale of data approximation, and show efficiency can be obtained even without sacrificing for effectiveness in practice with three analytic tasks: shortest paths/distances, network anomaly detection and link prediction.

Includes members:
  Member "graph pattern matching for dynamic team formation":
    also connected through other hyperedges:
      (none)
  Member "one pass error bounded trajectory simplification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gract a grammar based compressed index for trajectory data")
  Member "investigative simulation towards utilizing graph pattern matching for investigative search":
    also connected through other hyperedges:
      (none)
  Member "graph based anomaly detection and description a survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "bounded quadrant system error bounded trajectory compression on the go":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "one pass trajectory simplification using the synchronous euclidean distance")
  Member "shortest path and distance queries on road networks towards bridging theory and practice":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an introduction to graph data management")
  Member "capturing topology in graph pattern matching":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cgq relationship aware contextual graph querying in large networks")

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)