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: "parallel algorithms for select and partition with noisy comparisons"
  Abstract: We consider the problem of finding the $k^{th}$ highest element in a totally ordered set of $n$ elements (select), and partitioning a totally ordered set into the top $k$ and bottom $n-k$ elements (partition) using pairwise comparisons. Motivated by settings like peer grading or crowdsourcing, where multiple rounds of interaction are costly and queried comparisons may be inconsistent with the ground truth, we evaluate algorithms based both on their total runtime and the number of interactive rounds in three comparison models: noiseless (where the comparisons are correct), erasure (where comparisons are erased with probability $1-\gamma$), and noisy (where comparisons are correct with probability $1/2+\gamma/2$ and incorrect otherwise). We provide numerous matching upper and lower bounds in all three models. Even our results in the noiseless model, which is quite well-studied in the TCS literature on parallel algorithms, are novel.

Connected through hyperedge HE-1 (induced by source paper "locally private hypothesis selection"):
  with members:
    - "private hypothesis selection"
    - "pan private uniformity testing"
    - "sorted top k in rounds"
    - "improved differentially private analysis of variance"
    - "locally private learning without interaction requires separation"
    - "exponential separations in local differential privacy through communication complexity"
    - "the role of interactivity in local differential privacy"
    - "private identity testing for high dimensional distributions"

Connected through hyperedge HE-2 (induced by source paper "sorted top k in rounds"):
  with members:
    - "instance optimality in the noisy value and comparison model accept accept strong accept which papers get in"
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "competitive analysis of the top k ranking problem"
    - "adversarial top k ranking"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "active ranking using pairwise comparisons"
    - "sorting from noisy information"

Connected through hyperedge HE-3 (induced by source paper "stochastic submodular cover with limited adaptivity"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "tight bounds on the round complexity of the distributed maximum coverage problem"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "the stochastic matching problem beating half with a non adaptive algorithm"
    - "stochastic packing integer programs with few queries"
    - "scenario submodular cover"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "ignorance is almost bliss near optimal stochastic matching with few queries"

Connected through hyperedge HE-4 (induced by source paper "parallelization does not accelerate convex optimization adaptivity lower bounds for non smooth convex minimization"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "the importance of communities for learning to influence"
    - "an adaptivity hierarchy theorem for property testing"
    - "settling the query complexity of non adaptive junta testing"

Connected through hyperedge HE-5 (induced by source paper "secretary ranking with minimal inversions"):
  with members:
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "top k ranking from pairwise comparisons when spectral ranking is optimal"
    - "beyond matroids secretary problem and prophet inequality with general constraints"
    - "secretary problems with non uniform arrival order"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "pricing online decisions beyond auctions"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "on the k independence required by linear probing and minwise independence"

Connected through hyperedge HE-6 (induced by source paper "an exponential speedup in parallel running time for submodular maximization without loss in approximation"):
  with members:
    - "an adaptivity hierarchy theorem for property testing"
    - "settling the query complexity of non adaptive junta testing"
    - "the power of randomization distributed submodular maximization on massive datasets"
    - "noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization"
    - "welfare maximization with limited interaction"
    - "locally adaptive optimization adaptive seeding for monotone submodular functions"
    - "lazier than lazy greedy"
    - "the non adaptive query complexity of testing k parities"

Connected through hyperedge HE-7 (induced by source paper "non monotone submodular maximization in exponentially fewer iterations"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "an adaptivity hierarchy theorem for property testing"
    - "settling the query complexity of non adaptive junta testing"
    - "constrained submodular maximization beyond 1 e"
    - "the non adaptive query complexity of testing k parities"
    - "on the power of adaptivity in sparse recovery"
    - "submodular maximization by simulated annealing"

Connected through hyperedge HE-8 (induced by source paper "instance optimality in the noisy value and comparison model accept accept strong accept which papers get in"):
  with members:
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "nearly instance optimal sample complexity bounds for top k arm selection"
    - "skyline computation with noisy comparisons"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "instance optimal geometric algorithms"
    - "optimal adaptive algorithms for finding the nearest and farthest point on a parametric black box curve"

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)