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: "on the power of adaptivity in sparse recovery"
  Abstract: The goal of (stable) sparse recovery is to recover a $k$-sparse approximation $x*$ of a vector $x$ from linear measurements of $x$. Specifically, the goal is to recover $x*$ such that ||x-x*||_p <= C min_{k-sparse x'} ||x-x'||_q for some constant $C$ and norm parameters $p$ and $q$. It is known that, for $p=q=1$ or $p=q=2$, this task can be accomplished using $m=O(k \log (n/k))$ non-adaptive measurements [CRT06] and that this bound is tight [DIPW10,FPRU10,PW11]. #R##N#In this paper we show that if one is allowed to perform measurements that are adaptive, then the number of measurements can be considerably reduced. Specifically, for $C=1+eps$ and $p=q=2$ we show - A scheme with $m=O((1/eps)k log log (n eps/k))$ measurements that uses $O(log* k \log \log (n eps/k))$ rounds. This is a significant improvement over the best possible non-adaptive bound. - A scheme with $m=O((1/eps) k log (k/eps) + k \log (n/k))$ measurements that uses /two/ rounds. This improves over the best possible non-adaptive bound. To the best of our knowledge, these are the first results of this type. As an independent application, we show how to solve the problem of finding a duplicate in a data stream of $n$ items drawn from ${1, 2, ..., n-1}$ using $O(log n)$ bits of space and $O(log log n)$ passes, improving over the best possible space complexity achievable using a single pass.

Connected through hyperedge HE-1 (induced by source paper "stronger l2 l2 compressed sensing without iterating"):
  with members:
    - "dimension independent sparse fourier transform"
    - "solving linear programs in the current matrix multiplication time"
    - "improved algorithms for adaptive compressed sensing"
    - "sublinear time algorithms for compressive phase retrieval"
    - "one bit expandersketch for one bit compressed sensing"
    - "an adaptive sublinear time block sparse fourier transform"
    - "almost optimal phaseless compressed sensing with sublinear decoding time"
    - "sample efficient estimation and recovery in sparse fft via isolation on average"

Connected through hyperedge HE-2 (induced by source paper "querying a matrix through matrix vector products"):
  with members:
    - "tight query complexity lower bounds for pca via finite sample deformed wigner law"
    - "improved algorithms for adaptive compressed sensing"
    - "testing matrix rank optimally"
    - "near optimal linear decision trees for k sum and related problems"
    - "on estimating maximum matching size in graph streams"
    - "embeddings of schatten norms with applications to data streams"
    - "on approximating functions of the singular values in a stream"
    - "matrix norms in data streams faster multi pass and row order"

Connected through hyperedge HE-3 (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-4 (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"
    - "parallel algorithms for select and partition with noisy comparisons"
    - "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"

Connected through hyperedge HE-5 (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"
    - "parallel algorithms for select and partition with noisy comparisons"
    - "constrained submodular maximization beyond 1 e"
    - "the non adaptive query complexity of testing k parities"
    - "submodular maximization by simulated annealing"

Connected through hyperedge HE-6 (induced by source paper "batch sparse recovery or how to leverage the average sparsity"):
  with members:
    - "when distributed computation is communication expensive"
    - "lower bounds for adaptive sparse recovery"
    - "tight bounds for distributed functional monitoring"
    - "1 eps approximate sparse recovery"
    - "lower bounds for sparse recovery"
    - "approximate sparse recovery optimizing time and measurements"

Connected through hyperedge HE-7 (induced by source paper "improved algorithms for adaptive compressed sensing"):
  with members:
    - "sublinear time algorithms for compressive phase retrieval"
    - "on low risk heavy hitters and sparse recovery schemes"
    - "lower bounds for adaptive sparse recovery"
    - "1 eps approximate sparse recovery"
    - "approximate sparse recovery optimizing time and measurements"
    - "sequential adaptive compressed sampling via huffman codes"

Connected through hyperedge HE-8 (induced by source paper "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "the power of randomization distributed submodular maximization on massive datasets"
    - "a new framework for distributed submodular maximization"
    - "symmetry and approximability of submodular maximization problems"

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)