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: "how robust are linear sketches to adaptive inputs"
  Abstract: Linear sketches are powerful algorithmic tools that turn an n-dimensional input into a concise lower-dimensional representation via a linear transformation. Such sketches have seen a wide range of applications including norm estimation over data streams, compressed sensing, and distributed computing. In almost any realistic setting, however, a linear sketch faces the possibility that its inputs are correlated with previous evaluations of the sketch. Known techniques no longer guarantee the correctness of the output in the presence of such correlations. We therefore ask: Are linear sketches inherently non-robust to adaptively chosen inputs? We give a strong affirmative answer to this question. Specifically, we show that no linear sketch approximates the Euclidean norm of its input to within an arbitrary multiplicative approximation factor on a polynomial number of adaptively chosen inputs. The result remains true even if the dimension of the sketch is d = n - o(n) and the sketch is given unbounded computation time. Our result is based on an algorithm with running time polynomial in d that adaptively finds a distribution over inputs on which the sketch is incorrect with constant probability. Our result implies several corollaries for related problems including lp-norm estimation and compressed sensing. Notably, we resolve an open problem in compressed sensing regarding the feasibility of l2/l2-recovery guarantees in the presence of computationally bounded adversaries.

Connected through hyperedge HE-1 (induced by source paper "span recovery for deep neural networks with applications to input obfuscation"):
  with members:
    - "high fidelity extraction of neural network models"
    - "learning two layer rectified neural networks in polynomial time"
    - "learning two layer neural networks with symmetric inputs"
    - "adversarial attacks on neural network policies"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "electron proton dynamics in deep learning"
    - "learning neural networks with two nonlinear layers in polynomial time"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-2 (induced by source paper "the adversarial robustness of sampling"):
  with members:
    - "vc classes are adversarially robustly learnable but only improperly"
    - "introduction to online convex optimization"
    - "weighted reservoir sampling from distributed streams"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "stochastic bandits robust to adversarial corruptions"
    - "the everlasting database statistical validity at a fair price"
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "adaptive online learning in dynamic environments"

Connected through hyperedge HE-3 (induced by source paper "pseudo deterministic streaming"):
  with members:
    - "reproducibility and pseudo determinism in log space"
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "a note on unconditional subexponential time pseudo deterministic algorithms for bpp search problems"
    - "pseudo deterministic proofs"
    - "pseudodeterministic constructions in subexponential time"
    - "tight bounds for lp samplers finding duplicates in streams and related problems"
    - "streaming algorithms from precision sampling"

Connected through hyperedge HE-4 (induced by source paper "leveraging well conditioned bases streaming distributed summaries in minkowski p norms"):
  with members:
    - "frequent directions simple and deterministic matrix sketching"
    - "sketching as a tool for numerical linear algebra"
    - "quantile regression for large scale applications"
    - "relative errors for deterministic low rank matrix approximations"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
    - "the fast cauchy transform and faster robust linear regression"
    - "on the complexity of processing massive unordered distributed data"

Connected through hyperedge HE-5 (induced by source paper "high probability frequency moment sketches"):
  with members:
    - "maximum matching in turnstile streams"
    - "taylor polynomial estimator for estimating frequency moments"
    - "tight lower bound for linear sketches of moments"
    - "fast moment estimation in data streams in optimal space"

Connected through hyperedge HE-6 (induced by source paper "bloom filters adaptivity and the dictionary problem"):
  with members:
    - "adaptive cuckoo filters"
    - "fast and powerful hashing using tabulation"
    - "bloom filters in adversarial environments"
    - "don t thrash how to cache your hash on flash"
    - "backyard cuckoo hashing constant worst case operations with a succinct representation"
    - "an optimal bloom filter replacement based on matrix solving"

Connected through hyperedge HE-7 (induced by source paper "tight bounds for linear sketches of approximate matchings"):
  with members:
    - "welfare maximization with limited interaction"
    - "maximum matching in turnstile streams"
    - "sketching as a tool for numerical linear algebra"
    - "access to data and number of iterations dual primal algorithms for maximum matching under resource constraints"
    - "tight lower bound for linear sketches of moments"
    - "better bounds for matchings in the streaming model"
    - "superlinear lower bounds for multipass graph processing"
    - "maximum matching in semi streaming with few passes"

Connected through hyperedge HE-8 (induced by source paper "sketching as a tool for numerical linear algebra"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "optimal approximate matrix product in terms of stable rank"
    - "optimal cur matrix decompositions"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "uniform sampling for matrix approximation"
    - "single pass spectral sparsification in dynamic streams"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "subspace embeddings and ell_p regression using exponential random variables"

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)