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: "the complexity of computing the optimal composition of differential privacy"
  Abstract: In the study of differential privacy, composition theorems (starting with the original paper of Dwork, McSherry, Nissim, and Smith (TCC'06)) bound the degradation of privacy when composing several differentially private algorithms. Kairouz, Oh, and Viswanath (ICML'15) showed how to compute the optimal bound for composing $k$ arbitrary $(\epsilon,\delta)$-differentially private algorithms. We characterize the optimal composition for the more general case of $k$ arbitrary $(\epsilon_{1},\delta_{1}),\ldots,(\epsilon_{k},\delta_{k})$-differentially private algorithms where the privacy parameters may differ for each algorithm in the composition. We show that computing the optimal composition in general is $\#$P-complete. Since computing optimal composition exactly is infeasible (unless FP=$\#$P), we give an approximation algorithm that computes the composition to arbitrary accuracy in polynomial time. The algorithm is a modification of Dyer's dynamic programming approach to approximately counting solutions to knapsack problems (STOC'03).

Connected through hyperedge HE-1 (induced by source paper "gaussian differential privacy"):
  with members:
    - "bag of freebies for training object detection neural networks"
    - "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"
    - "bag of tricks for image classification with convolutional neural networks"
    - "privacy amplification by subsampling tight analyses via couplings and divergences"
    - "subsampled r enyi differential privacy and analytical moments accountant"
    - "renyi differential privacy"
    - "collecting telemetry data privately"
    - "concentrated differential privacy simplifications extensions and lower bounds"

Connected through hyperedge HE-2 (induced by source paper "the complexity of verifying loop free programs as differentially private"):
  with members:
    - "automated methods for checking differential privacy"
    - "property testing for differential privacy"
    - "bisimilarity distances for approximate differential privacy"
    - "detecting violations of differential privacy"
    - "synthesizing coupling proofs of differential privacy"
    - "lightdp towards automating differential privacy proofs"
    - "understanding the sparse vector technique for differential privacy"
    - "proving differential privacy via probabilistic couplings"

Connected through hyperedge HE-3 (induced by source paper "manipulation attacks in local differential privacy"):
  with members:
    - "distributed differential privacy via shuffling"
    - "practical locally private heavy hitters"
    - "optimal schemes for discrete distribution estimation under locally differential privacy"
    - "heavy hitters and the structure of local privacy"
    - "prochlo strong privacy for analytics in the crowd"
    - "local private efficient protocols for succinct histograms"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "the composition theorem for differential privacy"

Connected through hyperedge HE-4 (induced by source paper "optimal differential privacy composition for exponential mechanisms and the cost of adaptivity"):
  with members:
    - "practical differentially private top k selection with pay what you get composition"
    - "gaussian differential privacy"
    - "the role of interactivity in local differential privacy"
    - "privacy odometers and filters pay as you go composition"
    - "the composition theorem for differential privacy"
    - "calibrating data to sensitivity in private data analysis"
    - "what can we learn privately"

Connected through hyperedge HE-5 (induced by source paper "practical differentially private top k selection with pay what you get composition"):
  with members:
    - "pripearl a framework for privacy preserving analytics and reporting at linkedin"
    - "collecting telemetry data privately"
    - "concentrated differential privacy"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-6 (induced by source paper "differential privacy techniques for cyber physical systems a survey"):
  with members:
    - "quantifying differential privacy in continuous data release under temporal correlations"
    - "sok differential privacies"
    - "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "achieving differential privacy against non intrusive load monitoring in smart grid a fog computing approach"
    - "toward practical privacy preserving analytics for iot and cloud based healthcare systems"
    - "privacy preserving distributed deep learning for clinical data"
    - "re dpoctor real time health data releasing with w day differential privacy"

Connected through hyperedge HE-7 (induced by source paper "renyi differential privacy"):
  with members:
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "concentrated differential privacy"
    - "renyi divergence and kullback leibler divergence"
    - "the composition theorem for differential privacy"
    - "multiple source adaptation and the renyi divergence"
    - "calibrating data to sensitivity in private data analysis"
    - "lower bounds in differential privacy"

Connected through hyperedge HE-8 (induced by source paper "concentrated differential privacy simplifications extensions and lower bounds"):
  with members:
    - "concentrated differential privacy"
    - "simultaneous private learning of multiple concepts"
    - "between pure and approximate differential privacy"
    - "algorithmic stability for adaptive data analysis"
    - "private learning and sanitization pure vs approximate differential privacy"
    - "renyi divergence and kullback leibler divergence"
    - "interactive fingerprinting codes and the hardness of preventing false discovery"
    - "the composition theorem for differential 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.CC(Computational Complexity)