human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "how to refute a random csp"):
  with members:
    - "complexity theoretic limitations on learning halfspaces"
    - "sum of squares lower bounds from pairwise independence"
    - "tensor prediction rademacher complexity and random 3 xor"
    - "analysis of boolean functions"
    - "sum of squares proofs and the quest toward optimal algorithms"
    - "from average case complexity to improper learning complexity"
    - "more data speeds up training time in learning halfspaces over sparse vectors"
    - "on the complexity of random satisfiability problems with planted solutions"

Connected through hyperedge HE-2 (induced by source paper "spectral detection on sparse hypergraphs"):
  with members:
    - "spectral redemption in clustering sparse networks"
    - "graph spectra and the detectability of community structure in networks"
    - "a spectral approach to analysing belief propagation for 3 colouring"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-3 (induced by source paper "on the complexity of random satisfiability problems with planted solutions"):
  with members:
    - "how to refute a random csp"
    - "statistical query algorithms for stochastic convex optimization"
    - "human computable passwords"
    - "spectral redemption in clustering sparse networks"
    - "more data speeds up training time in learning halfspaces over sparse vectors"
    - "community detection thresholds and the weak ramanujan property"
    - "a complete characterization of statistical query learning with applications to evolvability"
    - "approximation resistant predicates from pairwise independence"

Question: Please tell me which class the center node 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)