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: "efficient algorithms for privately releasing marginals via convex relaxations"
  Abstract: Consider a database of $n$ people, each represented by a bit-string of length $d$ corresponding to the setting of $d$ binary attributes. A $k$-way marginal query is specified by a subset $S$ of $k$ attributes, and a $|S|$-dimensional binary vector $\beta$ specifying their values. The result for this query is a count of the number of people in the database whose attribute vector restricted to $S$ agrees with $\beta$. #R##N#Privately releasing approximate answers to a set of $k$-way marginal queries is one of the most important and well-motivated problems in differential privacy. Information theoretically, the error complexity of marginal queries is well-understood: the per-query additive error is known to be at least $\Omega(\min\{\sqrt{n},d^{\frac{k}{2}}\})$ and at most $\tilde{O}(\min\{\sqrt{n} d^{1/4},d^{\frac{k}{2}}\})$. However, no polynomial time algorithm with error complexity as low as the information theoretic upper bound is known for small $n$. In this work we present a polynomial time algorithm that, for any distribution on marginal queries, achieves average error at most $\tilde{O}(\sqrt{n} d^{\frac{\lceil k/2 \rceil}{4}})$. This error bound is as good as the best known information theoretic upper bounds for $k=2$. This bound is an improvement over previous work on efficiently releasing marginals when $k$ is small and when error $o(n)$ is desirable. Using private boosting we are also able to give nearly matching worst-case error bounds. #R##N#Our algorithms are based on the geometric techniques of Nikolov, Talwar, and Zhang. The main new ingredients are convex relaxations and careful use of the Frank-Wolfe algorithm for constrained convex minimization. To design our relaxations, we rely on the Grothendieck inequality from functional analysis.

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 "differential privacy on finite computers"):
  with members:
    - "priv it private and sample efficient identity testing"
    - "simultaneous private learning of multiple concepts"
    - "faster private release of marginals on small databases"
    - "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-3 (induced by source paper "grothendieck constant is norm of strassen matrix multiplication tensor"):
  with members:
    - "powers of tensors and fast matrix multiplication"
    - "nuclear norm of higher order tensors"
    - "grothendieck type inequalities in combinatorial optimization"
    - "sharp kernel clustering algorithms and their associated grothendieck inequalities"

Connected through hyperedge HE-4 (induced by source paper "private multiplicative weights beyond linear queries"):
  with members:
    - "more general queries and less generalization error in adaptive data analysis"
    - "preventing false discovery in interactive data analysis is hard"
    - "private learning and sanitization pure vs approximate differential privacy"
    - "preserving statistical validity in adaptive data analysis"
    - "faster private release of marginals on small databases"
    - "fingerprinting codes and the price of approximate differential privacy"
    - "calibrating data to sensitivity in private data analysis"
    - "the power of linear reconstruction attacks"

Connected through hyperedge HE-5 (induced by source paper "differentially private data releasing for smooth queries with synthetic database output"):
  with members:
    - "the noisy power method a meta algorithm with applications"
    - "faster algorithms for privately releasing marginals"
    - "differentially private online learning"
    - "a learning theory approach to non interactive database privacy"
    - "submodular functions are noise stable"
    - "private data release via learning thresholds"
    - "a simple and practical algorithm for differentially private data release"
    - "privately releasing conjunctions and the statistical query barrier"

Connected through hyperedge HE-6 (induced by source paper "private empirical risk minimization beyond the worst case the effect of the constraint set geometry"):
  with members:
    - "private multiplicative weights beyond linear queries"
    - "the geometry of differential privacy the sparse and approximate cases"
    - "fingerprinting codes and the price of approximate differential privacy"
    - "calibrating data to sensitivity in private data analysis"
    - "on the universality of online mirror descent"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-7 (induced by source paper "learning coverage functions and private release of marginals"):
  with members:
    - "agnostic learning of disjunctions on symmetric distributions"
    - "faster private release of marginals on small databases"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "optimal bounds on approximation of submodular and xos functions by juntas"
    - "representation approximation and learning of submodular functions using low rank decision trees"
    - "fingerprinting codes and the price of approximate differential privacy"
    - "testing coverage functions"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-8 (induced by source paper "faster private release of marginals on small databases"):
  with members:
    - "dual lower bounds for approximate degree and markov bernstein inequalities"
    - "the geometry of differential privacy the sparse and approximate cases"
    - "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"
    - "on arthur merlin games in communication complexity"
    - "a learning theory approach to non interactive database privacy"

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)