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 large margin mechanism for differentially private maximization"
  Abstract: A basic problem in the design of privacy-preserving algorithms is the private maximization problem: the goal is to pick an item from a universe that (approximately) maximizes a data-dependent function, all under the constraint of differential privacy. This problem has been used as a sub-routine in many privacy-preserving algorithms for statistics and machine-learning. #R##N#Previous algorithms for this problem are either range-dependent---i.e., their utility diminishes with the size of the universe---or only apply to very restricted function classes. This work provides the first general-purpose, range-independent algorithm for private maximization that guarantees approximate differential privacy. Its applicability is demonstrated on two fundamental tasks in data mining and machine learning.

Connected through hyperedge HE-1 (induced by source paper "free gap information from the differentially private sparse vector and noisy max mechanisms"):
  with members:
    - "proving differential privacy with shadow execution"
    - "private selection from private candidates"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "ektelo a framework for defining differentially private computations"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "synthesizing coupling proofs of differential privacy"
    - "renyi differential privacy"
    - "towards practical differential privacy for sql queries"

Connected through hyperedge HE-2 (induced by source paper "private pac learning implies finite littlestone dimension"):
  with members:
    - "model agnostic private learning via stability"
    - "privacy preserving prediction"
    - "adaptive learning with robust generalization guarantees"
    - "simultaneous private learning of multiple concepts"
    - "differentially private release and learning of threshold functions"
    - "algorithmic stability for adaptive data analysis"
    - "characterizing the sample complexity of private learners"
    - "sample complexity bounds on differentially private learning via communication complexity"

Connected through hyperedge HE-3 (induced by source paper "private selection from private candidates"):
  with members:
    - "supervising unsupervised learning"
    - "differentially private release and learning of threshold functions"
    - "algorithmic stability for adaptive data analysis"
    - "private learning and sanitization pure vs approximate differential privacy"
    - "preserving statistical validity in adaptive data analysis"
    - "calibrating data to sensitivity in private data analysis"
    - "differentially private combinatorial optimization"
    - "what can we learn privately"

Connected through hyperedge HE-4 (induced by source paper "advanced probabilistic couplings for differential privacy"):
  with members:
    - "coupling proofs are probabilistic product programs"
    - "lightdp towards automating differential privacy proofs"
    - "understanding the sparse vector technique for differential privacy"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "a program logic for union bounds"
    - "make up your mind the price of online queries in differential privacy"
    - "privacy odometers and filters pay as you go composition"
    - "proving differential privacy via probabilistic couplings"

Connected through hyperedge HE-5 (induced by source paper "pufferfish privacy mechanisms for correlated data"):
  with members:
    - "inferential privacy guarantees for differentially private mechanisms"
    - "protecting locations with differential privacy under temporal correlations"
    - "differentially private empirical risk minimization efficient algorithms and tight error bounds"
    - "blowfish privacy tuning privacy utility trade offs using policies"
    - "differentially private algorithms for empirical machine learning"
    - "calibrating data to sensitivity in private data analysis"
    - "an adaptive mechanism for accurate query answering under differential privacy"
    - "beyond worst case analysis in private singular vector computation"

Connected through hyperedge HE-6 (induced by source paper "privacy preserving analysis of correlated data"):
  with members:
    - "beyond worst case analysis in private singular vector computation"
    - "differentially private online learning"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-7 (induced by source paper "differentially private release and learning of threshold functions"):
  with members:
    - "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"
    - "optimal private halfspace counting via discrepancy"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-8 (induced by source paper "order revealing encryption and the hardness of private learning"):
  with members:
    - "differentially private release and learning of threshold functions"
    - "sample complexity bounds on differentially private learning via communication complexity"
    - "private learning and sanitization pure vs approximate differential privacy"
    - "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"
    - "faster algorithms for privately releasing marginals"
    - "answering n 2 o 1 counting queries with differential privacy is hard"

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.LG(Machine Learning)