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 sampling for better osn data provisioning"):
  Abstract: Data concerning the users and usage of Online Social Networks (OSNs) has become available externally, from public resources (e.g., user profiles), participation in OSNs (e.g., establishing relationships and recording transactions such as user updates) and APIs of the OSN provider (such as the Twitter API). APIs let OSN providers monetize the release of data while helping control measurement load, e.g. by providing samples with different cost-granularity tradeoffs. To date, this approach has been more suited to releasing transactional data, with graphical data still being obtained by resource intensive methods such a graph crawling. In this paper, we propose a method for OSNs to provide samples of the user graph of tunable size, in non-intersecting increments, with sample selection that can be weighted to enhance accuracy when estimating different features of the graph.

Includes members:
  Member "walking on a graph with a magnifying glass stratified sampling via weighted random walks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "star sampling with and without replacement")
  Member "estimating and sampling graphs with multidimensional random walks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "graphact accelerating gcn training on cpu fpga heterogeneous platforms")
  Member "stream sampling for variance optimal estimation of subset sums":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the adversarial robustness of sampling")
  Member "social networks that matter twitter under the microscope":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "consistent recovery threshold of hidden nearest neighbor 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.DS(Data Structures and Algorithms)