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: "faster private release of marginals on small databases"
  Abstract: We study the problem of answering \emph{$k$-way marginal} queries on a database $D \in (\{0,1\}^d)^n$, while preserving differential privacy. The answer to a $k$-way marginal query is the fraction of the database's records $x \in \{0,1\}^d$ with a given value in each of a given set of up to $k$ columns. Marginal queries enable a rich class of statistical analyses on a dataset, and designing efficient algorithms for privately answering marginal queries has been identified as an important open problem in private data analysis. #R##N#For any $k$, we give a differentially private online algorithm that runs in time $$ \min{\exp(d^{1-\Omega(1/\sqrt{k})}), \exp(d / \log^{.99} d)\} $$ per query and answers any (possibly superpolynomially long and adaptively chosen) sequence of $k$-way marginal queries up to error at most $\pm .01$ on every query, provided $n \gtrsim d^{.51} $. To the best of our knowledge, this is the first algorithm capable of privately answering marginal queries with a non-trivial worst-case accuracy guarantee on a database of size $\poly(d, k)$ in time $\exp(o(d))$. #R##N#Our algorithms are a variant of the private multiplicative weights algorithm (Hardt and Rothblum, FOCS '10), but using a different low-weight representation of the database. We derive our low-weight representation using approximations to the OR function by low-degree polynomials with coefficients of bounded $L_1$-norm. We also prove a strong limitation on our approach that is of independent approximation-theoretic interest. Specifically, we show that for any $k = o(\log d)$, any polynomial with coefficients of $L_1$-norm $poly(d)$ that pointwise approximates the $d$-variate OR function on all inputs of Hamming weight at most $k$ must have degree $d^{1-O(1/\sqrt{k})}$.

Connected through hyperedge HE-1 (induced by source paper "the power of factorization mechanisms in local and central differential privacy"):
  with members:
    - "differentially private sql with bounded user contribution"
    - "optimizing error of high dimensional statistical queries under differential privacy"
    - "towards instance optimal private query release"
    - "towards practical differential privacy for sql queries"
    - "heavy hitters and the structure of local privacy"
    - "prochlo strong privacy for analytics in the crowd"
    - "an improved private mechanism for small databases"
    - "rappor randomized aggregatable privacy preserving ordinal response"

Connected through hyperedge HE-2 (induced by source paper "approximate degree secret sharing and concentration phenomena"):
  with members:
    - "near optimal lower bounds on the threshold degree and sign rank of ac 0"
    - "a nearly optimal lower bound on the approximate degree of ac 0"
    - "dual lower bounds for approximate degree and markov bernstein inequalities"
    - "hardness amplification and the approximate degree of constant depth circuits"
    - "on the number of iterations for dantzig wolfe optimization and packing covering approximation algorithms"

Connected through hyperedge HE-3 (induced by source paper "the hardest halfspace"):
  with members:
    - "optimal bounds for sign representing the intersection of two halfspaces by polynomials"

Connected through hyperedge HE-4 (induced by source paper "how to use heuristics for differential privacy"):
  with members:
    - "unleashing linear optimizers for group fair learning and optimization"
    - "model agnostic private learning via stability"
    - "property testing for differential privacy"
    - "private pac learning implies finite littlestone dimension"
    - "privacy preserving prediction"
    - "online learning via differential privacy"
    - "adaptive learning with robust generalization guarantees"
    - "efficient algorithms for adversarial contextual learning"

Connected through hyperedge HE-5 (induced by source paper "a nearly optimal lower bound on the approximate degree of ac 0"):
  with members:
    - "on szk and pp"
    - "separations in query complexity based on pointer functions"
    - "dual polynomials for collision and element distinctness"
    - "distribution independent reliable learning"
    - "dual lower bounds for approximate degree and markov bernstein inequalities"
    - "hardness amplification and the approximate degree of constant depth circuits"
    - "faster algorithms for privately releasing marginals"
    - "a separation of np and conp in multiparty communication complexity"

Connected through hyperedge HE-6 (induced by source paper "differential privacy on finite computers"):
  with members:
    - "priv it private and sample efficient identity testing"
    - "simultaneous private learning of multiple concepts"
    - "efficient algorithms for privately releasing marginals via convex relaxations"
    - "preserving differential privacy under finite precision semantics"
    - "calibrating data to sensitivity in private data analysis"
    - "faster algorithms for privately releasing marginals"
    - "a learning theory approach to non interactive database privacy"
    - "submodular functions are noise stable"

Connected through hyperedge HE-7 (induced by source paper "strong hardness of privacy from weak traitor tracing"):
  with members:
    - "order revealing encryption and the hardness of private learning"
    - "differentially private release and learning of threshold functions"
    - "between pure and approximate differential privacy"
    - "preventing false discovery in interactive data analysis is hard"
    - "private learning and sanitization pure vs approximate differential privacy"
    - "private multiplicative weights beyond linear queries"
    - "interactive fingerprinting codes and the hardness of preventing false discovery"
    - "the geometry of differential privacy the sparse and approximate cases"

Connected through hyperedge HE-8 (induced by source paper "order revealing encryption and the hardness of private learning"):
  with members:
    - "differentially private release and learning of threshold functions"
    - "the large margin mechanism for differentially private maximization"
    - "sample complexity bounds on differentially private learning via communication complexity"
    - "private learning and sanitization pure vs approximate differential privacy"
    - "fingerprinting codes and the price of approximate differential privacy"
    - "calibrating data to sensitivity in private data analysis"
    - "faster algorithms for privately releasing marginals"
    - "answering n 2 o 1 counting queries with differential privacy is hard"

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)