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 "subsampled power iteration a unified algorithm for block models and planted csp s"):
  Abstract: We present an algorithm for recovering planted solutions in two well-known models, the stochastic block model and planted constraint satisfaction problems, via a common generalization in terms of random bipartite graphs. Our algorithm matches up to a constant factor the best-known bounds for the number of edges (or constraints) needed for perfect recovery and its running time is linear in the number of edges used. The time complexity is significantly better than both spectral and SDP-based approaches. #R##N#The main contribution of the algorithm is in the case of unequal sizes in the bipartition (corresponding to odd uniformity in the CSP). Here our algorithm succeeds at a significantly lower density than the spectral approaches, surpassing a barrier based on the spectral norm of a random matrix. #R##N#Other significant features of the algorithm and analysis include (i) the critical use of power iteration with subsampling, which might be of independent interest; its analysis requires keeping track of multiple norms of an evolving solution (ii) it can be implemented statistically, i.e., with very limited access to the input distribution (iii) the algorithm is extremely simple to implement and runs in linear time, and thus is practical even for very large instances.

Includes members:
  Member "exact recovery in the stochastic block model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "renyi entropy bounds on the active learning cost performance tradeoff")
  Member "spectral redemption in clustering sparse networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "more data speeds up training time in learning halfspaces over sparse vectors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "time space tradeoffs for distinguishing distributions and applications to security of goldreich s prg")
  Member "on the complexity of random satisfiability problems with planted solutions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sum of squares meets square loss fast rates for agnostic tensor completion")
  Member "community detection thresholds and the weak ramanujan property":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "from tree matching to sparse graph alignment")
  Member "graph spectra and the detectability of community structure in networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fragility of spectral clustering for networks with an overlapping structure")
  Member "using more data to speed up training time":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the computational power of optimization in online learning")
  Member "hiding satisfying assignments two are better than one":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "community based 3 sat formulas with a predefined solution")

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)