human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "fingerprinting codes and the price of approximate differential privacy"):
  Abstract: We show new lower bounds on the sample complexity of $(\varepsilon, \delta)$-differentially private algorithms that accurately answer large sets of counting queries. A counting query on a database $D \in (\{0,1\}^d)^n$ has the form "What fraction of the individual records in the database satisfy the property $q$?" We show that in order to answer an arbitrary set $\mathcal{Q}$ of $\gg nd$ counting queries on $D$ to within error $\pm \alpha$ it is necessary that $$ n \geq \tilde{\Omega}\Bigg(\frac{\sqrt{d} \log |\mathcal{Q}|}{\alpha^2 \varepsilon} \Bigg). $$ This bound is optimal up to poly-logarithmic factors, as demonstrated by the Private Multiplicative Weights algorithm (Hardt and Rothblum, FOCS'10). In particular, our lower bound is the first to show that the sample complexity required for accuracy and $(\varepsilon, \delta)$-differential privacy is asymptotically larger than what is required merely for accuracy, which is $O(\log |\mathcal{Q}| / \alpha^2)$. In addition, we show that our lower bound holds for the specific case of $k$-way marginal queries (where $|\mathcal{Q}| = 2^k \binom{d}{k}$) when $\alpha$ is not too small compared to $d$ (e.g. when $\alpha$ is any fixed constant). #R##N#Our results rely on the existence of short \emph{fingerprinting codes} (Boneh and Shaw, CRYPTO'95, Tardos, STOC'03), which we show are closely connected to the sample complexity of differentially private data release. We also give a new method for combining certain types of sample complexity lower bounds into stronger lower bounds.

Includes members:
  Member "preventing false discovery in interactive data analysis is hard":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reasoning about generalization via conditional mutual information")
  Member "characterizing the sample complexity of private learners":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the power of synergy in differential privacy combining a small curator with local randomizers")
  Member "private learning and sanitization pure vs approximate differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an equivalence between private classification and online prediction")
  Member "space lower bounds for itemset frequency sketches":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "finding subcube heavy hitters in analytics data streams")
  Member "faster private release of marginals on small databases":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the power of factorization mechanisms in local and central differential privacy")
  Member "the geometry of differential privacy the sparse and approximate cases":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pure differentially private summation from anonymous messages")
  Member "efficient algorithms for privately releasing marginals via convex relaxations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the power of factorization mechanisms in local and central differential privacy")
  Member "calibrating data to sensitivity in private data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "locally private hypothesis selection")

Question: Please tell me which class the source paper 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.CR(Cryptography and Security)