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 "multiresolution cube estimators for sensor network aggregate queries"):
  Abstract: In this work we present in-network techniques to improve the efficiency of spatial aggregate queries. Such queries are very common in a sensornet setting, demanding more targeted techniques for their handling. Our approach constructs and maintains multi-resolution cube hierarchies inside the network, which can be constructed in a distributed fashion. In case of failures, recovery can also be performed with in-network decisions. In this paper we demonstrate how in-network cube hierarchies can be used to summarize sensor data, and how they can be exploited to improve the efficiency of spatial aggregate queries. We show that query plans over our cube summaries can be computed in polynomial time, and we present a PTIME algorithm that selects the minimum number of data requests that can compute the answer to a spatial query. We further extend our algorithm to handle optimization over multiple queries, which can also be done in polynomial time. We discuss enriching cube hierarchies with extra summary information, and present an algorithm for distributed cube construction. Finally we investigate node and area failures, and algorithms to recover query results.

Includes members:
  Member "fountain codes based distributed storage algorithms for large scale wireless sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust node estimation and topology discovery algorithm in large scale wireless sensor networks")
  Member "decentralized erasure codes for distributed networked storage":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improving the secrecy of distributed storage systems using interference alignment")

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)