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: "differentially private combinatorial optimization"
  Abstract: Consider the following problem: given a metric space, some of whose points are "clients", open a set of at most $k$ facilities to minimize the average distance from the clients to these facilities. This is just the well-studied $k$-median problem, for which many approximation algorithms and hardness results are known. Note that the objective function encourages opening facilities in areas where there are many clients, and given a solution, it is often possible to get a good idea of where the clients are located. However, this poses the following quandary: what if the identity of the clients is sensitive information that we would like to keep private? Is it even possible to design good algorithms for this problem that preserve the privacy of the clients? #R##N#In this paper, we initiate a systematic study of algorithms for discrete optimization problems in the framework of differential privacy (which formalizes the idea of protecting the privacy of individual input elements). We show that many such problems indeed have good approximation algorithms that preserve differential privacy; this is even in cases where it is impossible to preserve cryptographic definitions of privacy while computing any non-trivial approximation to even the_value_ of an optimal solution, let alone the entire solution. #R##N#Apart from the $k$-median problem, we study the problems of vertex and set cover, min-cut, facility location, Steiner tree, and the recently introduced submodular maximization problem, "Combinatorial Public Projects" (CPP).

Connected through hyperedge HE-1 (induced by source paper "protect edge privacy in path publishing with differential privacy"):
  with members:
    - "priste from location privacy to spatiotemporal event privacy"
    - "geographic differential privacy for mobile crowd coverage maximization"
    - "protecting locations with differential privacy under temporal correlations"
    - "calibrating data to sensitivity in private data analysis"
    - "vector field k means clustering trajectories by fitting multiple vector fields"

Connected through hyperedge HE-2 (induced by source paper "differentially private combinatorial cloud auction"):
  with members:
    - "differentially private double spectrum auction with approximate social welfare maximization"
    - "auction mechanisms in cloud fog computing resource allocation for public blockchain networks"
    - "dominant resource fairness in cloud computing systems with heterogeneous servers"

Connected through hyperedge HE-3 (induced by source paper "differentially private algorithms for learning mixtures of separated gaussians"):
  with members:
    - "private hypothesis selection"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "privately learning high dimensional distributions"
    - "differentially private k means with constant multiplicative error"
    - "inspectre privately estimating the unseen"
    - "optimal schemes for discrete distribution estimation under locally differential privacy"
    - "finite sample differentially private confidence intervals"
    - "clustering algorithms for the centralized and local models"

Connected through hyperedge HE-4 (induced by source paper "differentially private mixed type data generation for unsupervised learning"):
  with members:
    - "dp cgan differentially private synthetic data and label generation"
    - "differentially private learning with adaptive clipping"
    - "differentially private model publishing for deep learning"
    - "differentially private generative adversarial networks for time series continuous and discrete open data"
    - "private selection from private candidates"
    - "differentially private generative adversarial network"
    - "differentially private data generative models"
    - "generating artificial data for private deep learning"

Connected through hyperedge HE-5 (induced by source paper "differentially private learning with adaptive clipping"):
  with members:
    - "exploiting unintended feature leakage in collaborative learning"
    - "private selection from private candidates"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "a general approach to adding differential privacy to iterative training procedures"
    - "learning differentially private recurrent language models"
    - "deep learning techniques for music generation a survey"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-6 (induced by source paper "locally private k means clustering"):
  with members:
    - "practical locally private heavy hitters"
    - "clustering algorithms for the centralized and local models"
    - "heavy hitters and the structure of local privacy"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "locating a small cluster privately"
    - "k variates more pluses in the k means"
    - "differentially private k means clustering"
    - "distributed private heavy hitters"

Connected through hyperedge HE-7 (induced by source paper "automated methods for checking differential privacy"):
  with members:
    - "bisimilarity distances for approximate differential privacy"
    - "detecting violations of differential privacy"
    - "synthesizing coupling proofs of differential privacy"
    - "advanced probabilistic couplings for differential privacy"
    - "understanding the sparse vector technique for differential privacy"
    - "a program logic for union bounds"
    - "proving differential privacy via probabilistic couplings"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-8 (induced by source paper "differentially private learning of geometric concepts"):
  with members:
    - "privacy preserving prediction"
    - "simultaneous private learning of multiple concepts"
    - "order revealing encryption and the hardness of private learning"
    - "differentially private release and learning of threshold functions"
    - "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"
    - "learning privately with labeled and unlabeled examples"

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)