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: "optimal private halfspace counting via discrepancy"
  Abstract: A range counting problem is specified by a set $P$ of size $|P| = n$ of points in $\mathbb{R}^d$, an integer weight $x_p$ associated to each point $p \in P$, and a range space ${\cal R} \subseteq 2^{P}$. Given a query range $R \in {\cal R}$, the target output is $R(\vec{x}) = \sum_{p \in R}{x_p}$. Range counting for different range spaces is a central problem in Computational Geometry. #R##N#We study $(\epsilon, \delta)$-differentially private algorithms for range counting. Our main results are for the range space given by hyperplanes, that is, the halfspace counting problem. We present an $(\epsilon, \delta)$-differentially private algorithm for halfspace counting in $d$ dimensions which achieves $O(n^{1-1/d})$ average squared error. This contrasts with the $\Omega(n)$ lower bound established by the classical result of Dinur and Nissim [PODS 2003] for arbitrary subset counting queries. We also show a matching lower bound on average squared error for any $(\epsilon, \delta)$-differentially private algorithm for halfspace counting. Both bounds are obtained using discrepancy theory. For the lower bound, we use a modified discrepancy measure and bound approximation of $(\epsilon, \delta)$-differentially private algorithms for range counting queries in terms of this discrepancy. We also relate the modified discrepancy measure to classical combinatorial discrepancy, which allows us to exploit known discrepancy lower bounds. This approach also yields a lower bound of $\Omega((\log n)^{d-1})$ for $(\epsilon, \delta)$-differentially private orthogonal range counting in $d$ dimensions, the first known superconstant lower bound for this problem. For the upper bound, we use an approach inspired by partial coloring methods for proving discrepancy upper bounds, and obtain $(\epsilon, \delta)$-differentially private algorithms for range counting with polynomially bounded shatter function range spaces.

Connected through hyperedge HE-1 (induced by source paper "on the power of multiple anonymous messages"):
  with members:
    - "improved summation from shuffling"
    - "separating local shuffled differential privacy via histograms"
    - "the privacy blanket of the shuffle model"
    - "differentially private summation with multi message shuffling"
    - "practical and robust privacy amplification with multi party differential privacy"
    - "communication complexity in locally private distribution estimation and heavy hitters"
    - "private aggregation from fewer anonymous messages"
    - "scalable and differentially private distributed aggregation in the shuffled model"

Connected through hyperedge HE-2 (induced by source paper "set codes with small intersections and small discrepancies"):
  with members:
    - "asymmetric lee distance codes for dna based storage"
    - "codes for dna sequence profiles"
    - "approximating bin packing within o log opt log log opt bins"

Connected through hyperedge HE-3 (induced by source paper "tight lower bounds for differentially private selection"):
  with members:
    - "the price of selection in differential privacy"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "make up your mind the price of online queries in differential privacy"
    - "between pure and approximate differential privacy"
    - "algorithmic stability for adaptive data analysis"
    - "preventing false discovery in interactive data analysis is hard"
    - "private multiplicative weights beyond linear queries"
    - "interactive fingerprinting codes and the hardness of preventing false discovery"

Connected through hyperedge HE-4 (induced by source paper "differentially private release and learning of threshold functions"):
  with members:
    - "the large margin mechanism for differentially private maximization"
    - "characterizing the sample complexity of private learners"
    - "sample complexity bounds on differentially private learning via communication complexity"
    - "private learning and sanitization pure vs approximate differential privacy"
    - "faster and sample near optimal algorithms for proper learning mixtures of gaussians"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "an improved private mechanism for small databases"):
  with members:
    - "randomized rounding for the largest simplex problem"
    - "the geometry of differential privacy the sparse and approximate cases"
    - "approximating hereditary discrepancy via small width ellipsoids"
    - "fingerprinting codes and the price of approximate differential privacy"
    - "calibrating data to sensitivity in private data analysis"
    - "answering n 2 o 1 counting queries with differential privacy is hard"
    - "a learning theory approach to non interactive database privacy"
    - "iterative constructions and private data release"

Connected through hyperedge HE-6 (induced by source paper "on the relation between identifiability differential privacy and mutual information privacy"):
  with members:
    - "a minimax distortion view of differentially private query release"
    - "utility privacy tradeoffs in databases an information theoretic approach"
    - "fingerprinting codes and the price of approximate differential privacy"
    - "calibrating data to sensitivity in private data analysis"
    - "privacy against statistical inference"
    - "a learning theory approach to non interactive database privacy"
    - "differential privacy on the trade off between utility and information leakage"
    - "lower bounds in differential privacy"

Connected through hyperedge HE-7 (induced by source paper "a minimax distortion view of differentially private query release"):
  with members:
    - "on the relation between identifiability differential privacy and mutual information privacy"
    - "dual query practical private query release for high dimensional data"
    - "calibrating data to sensitivity in private data analysis"
    - "the johnson lindenstrauss transform itself preserves differential privacy"
    - "answering n 2 o 1 counting queries with differential privacy is hard"
    - "a learning theory approach to non interactive database privacy"
    - "iterative constructions and private data release"
    - "a simple and practical algorithm for differentially private data release"

Connected through hyperedge HE-8 (induced by source paper "computer aided proof of erdos discrepancy properties"):
  with members:
    - "a sat attack on the erdos discrepancy conjecture"
    - "on the erdos discrepancy problem"

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)