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: "proving differential privacy in hoare logic"
  Abstract: Differential privacy is a rigorous, worst-case notion of privacy-preserving computation. Informally, a probabilistic program is differentially private if the participation of a single individual in the input database has a limited effect on the program's distribution on outputs. More technically, differential privacy is a quantitative 2-safety property that bounds the distance between the output distributions of a probabilistic program on adjacent inputs. Like many 2-safety properties, differential privacy lies outside the scope of traditional verification techniques. Existing approaches to enforce privacy are based on intricate, non-conventional type systems, or customized relational logics. These approaches are difficult to implement and often cumbersome to use. We present an alternative approach that verifies differential privacy by standard, non-relational reasoning on non-probabilistic programs. Our approach transforms a probabilistic program into a non-probabilistic program which simulates two executions of the original program. We prove that if the target program is correct with respect to a Hoare specification, then the original probabilistic program is differentially private. We provide a variety of examples from the differential privacy literature to demonstrate the utility of our approach. Finally, we compare our approach with existing verification techniques for privacy.

Connected through hyperedge HE-1 (induced by source paper "guidelines for implementing and auditing differentially private systems"):
  with members:
    - "differentially private objective perturbation beyond smoothness and convexity"
    - "identifying cache based side channels through secret augmented abstract interpretation"
    - "fuzzi a three level logic for differential privacy"
    - "an economic analysis of privacy protection and statistical accuracy as social choices"
    - "private selection from private candidates"
    - "ektelo a framework for defining differentially private computations"
    - "detecting violations of differential privacy"
    - "how to use heuristics for differential privacy"

Connected through hyperedge HE-2 (induced by source paper "a programming framework for differential privacy with accuracy concentration bounds"):
  with members:
    - "fuzzi a three level logic for differential privacy"
    - "proving differential privacy with shadow execution"
    - "ektelo a framework for defining differentially private computations"
    - "synthesizing coupling proofs of differential privacy"
    - "apex accuracy aware differentially private data exploration"
    - "renyi differential privacy"
    - "lightdp towards automating differential privacy proofs"
    - "advanced probabilistic couplings for differential privacy"

Connected through hyperedge HE-3 (induced by source paper "proving differential privacy with shadow execution"):
  with members:
    - "property testing for differential privacy"
    - "ektelo a framework for defining differentially private computations"
    - "detecting violations of differential privacy"
    - "synthesizing coupling proofs of differential privacy"
    - "relational symbolic execution"
    - "renyi differential privacy"
    - "towards practical differential privacy for sql queries"
    - "lightdp towards automating differential privacy proofs"

Connected through hyperedge HE-4 (induced by source paper "approximate span liftings compositional semantics for relaxations of differential privacy"):
  with members:
    - "synthesizing coupling proofs of differential privacy"
    - "renyi differential privacy"
    - "liftings for differential privacy"
    - "lightdp towards automating differential privacy proofs"
    - "advanced probabilistic couplings for differential privacy"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "approximate relational hoare logic for continuous random samplings"
    - "differentially private bayesian programming"

Connected through hyperedge HE-5 (induced by source paper "statistical epistemic logic"):
  with members:
    - "local obfuscation mechanisms for hiding probability distributions"
    - "hybrid statistical estimation of mutual information and its application to information flow"
    - "towards logical specification of statistical machine learning"
    - "hyperpctl a temporal logic for probabilistic hyperproperties"
    - "on the anonymization of differentially private location obfuscation"
    - "statistical model checking an overview"
    - "an epistemic approach to coercion resistance for electronic voting protocols"
    - "anonymity and information hiding in multiagent systems"

Connected through hyperedge HE-6 (induced by source paper "detecting violations of differential privacy"):
  with members:
    - "synthesizing coupling proofs of differential privacy"
    - "relational symbolic execution"
    - "renyi differential privacy"
    - "lightdp towards automating differential privacy proofs"
    - "advanced probabilistic couplings for differential privacy"
    - "understanding the sparse vector technique for differential privacy"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "concentrated differential privacy"

Connected through hyperedge HE-7 (induced by source paper "property testing for differential privacy"):
  with members:
    - "detecting violations of differential privacy"
    - "renyi differential privacy"
    - "collecting telemetry data privately"
    - "which distribution distances are sublinearly testable"
    - "concentrated differential privacy"
    - "optimal testing for properties of distributions"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "differential privacy and machine learning a survey and review"

Connected through hyperedge HE-8 (induced by source paper "bounded expectations resource analysis for probabilistic programs"):
  with members:
    - "verifying and synthesizing constant resource implementations with types"
    - "how long o bayesian network will i sample thee a program analysis perspective on expected sampling times"
    - "automated recurrence analysis for almost linear expected runtime bounds"
    - "non polynomial worst case analysis of recursive programs"
    - "a program logic for union bounds"
    - "termination analysis of probabilistic programs through positivstellensatz s"
    - "weakest precondition reasoning for expected run times of probabilistic programs"
    - "towards automatic resource bound analysis for ocaml"

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.LO(Logic in Computer Science)