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 "streaming graph challenge stochastic block partition"):
  Abstract: An important objective for analyzing real-world graphs is to achieve scalable performance on large, streaming graphs. A challenging and relevant example is the graph partition problem. As a combinatorial problem, graph partition is NP-hard, but existing relaxation methods provide reasonable approximate solutions that can be scaled for large graphs. Competitive benchmarks and challenges have proven to be an effective means to advance state-of-the-art performance and foster community collaboration. This paper describes a graph partition challenge with a baseline partition algorithm of sub-quadratic complexity. The algorithm employs rigorous Bayesian inferential methods based on a statistical model that captures characteristics of the real-world graphs. This strong foundation enables the algorithm to address limitations of well-known graph partition approaches such as modularity maximization. This paper describes various aspects of the challenge including: (1) the data sets and streaming graph generator, (2) the baseline partition algorithm with pseudocode, (3) an argument for the correctness of parallelizing the Bayesian inference, (4) different parallel computation strategies such as node-based parallelism and matrix-based parallelism, (5) evaluation metrics for partition correctness and computational requirements, (6) preliminary timing of a Python-based demonstration code and the open source C++ code, and (7) considerations for partitioning the graph in streaming fashion. Data sets and source code for the algorithm as well as metrics, with detailed documentation are available at GraphChallenge.org.

Includes members:
  Member "static graph challenge subgraph isomorphism":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a systematic survey of general sparse matrix matrix multiplication")
  Member "pagerank pipeline benchmark proposal for a holistic system benchmark for big data platforms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sparse deep neural network graph challenge")
  Member "ensuring rapid mixing and low bias for asynchronous gibbs sampling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed metropolis sampler with optimal parallelism")
  Member "a high performance implementation of spectral clustering on cpu gpu platforms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bandwidth optimized parallel algorithms for sparse matrix matrix multiplication using propagation blocking")
  Member "an incremental local first community detection method for dynamic graphs":
    also connected through other hyperedges:
      (none)
  Member "fast detection of overlapping communities via online tensor methods":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "guaranteed scalable learning of latent tree models")
  Member "bayesian discovery of threat networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "influence estimation on social media networks using causal inference")
  Member "network sampling from static to streaming graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "understanding the limitations of network online learning")

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)