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 "spinner scalable graph partitioning in the cloud"):
  Abstract: Several organizations, like social networks, store and routinely analyze large graphs as part of their daily operation. Such graphs are typically distributed across multiple servers, and graph partitioning is critical for efficient graph management. Existing partitioning algorithms focus on finding graph partitions with good locality, but disregard the pragmatic challenges of integrating partitioning into large-scale graph management systems deployed on the cloud, such as dealing with the scale and dynamicity of the graph and the compute environment. #R##N#In this paper, we propose Spinner, a scalable and adaptive graph partitioning algorithm based on label propagation designed on top of the Pregel model. Spinner scales to massive graphs, produces partitions with locality and balance comparable to the state-of-the-art and efficiently adapts the partitioning upon changes. We describe our algorithm and its implementation in the Pregel programming model that makes it possible to partition billion-vertex graphs. We evaluate Spinner with a variety of synthetic and real graphs and show that it can compute partitions with quality comparable to the state-of-the art. In fact, by using Spinner in conjunction with the Giraph graph processing engine, we speed up different applications by a factor of 2 relative to standard hash partitioning.

Includes members:
  Member "distributed balanced partitioning via linear embedding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi dimensional balanced graph partitioning via projected gradient descent")
  Member "parallel graph partitioning for complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multilevel acyclic hypergraph partitioning")
  Member "evolution of social attribute networks measurements modeling and implications using google":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "defending against machine learning based inference attacks via adversarial examples opportunities and challenges")
  Member "defining and evaluating network communities based on ground truth":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "certified robustness of community detection against adversarial structural perturbation via randomized smoothing")

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.DC(Distributed, Parallel, and Cluster Computing)