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: "answering n 2 o 1 counting queries with differential privacy is hard"
  Abstract: A central problem in differentially private data analysis is how to design efficient algorithms capable of answering large numbers of counting queries on a sensitive database. Counting queries of the form "What fraction of individual records in the database satisfy the property q?" We prove that if one-way functions exist, then there is no algorithm that takes as input a database D in ({0,1}^d)^n, and k = n^{2+o(1)} arbitrary efficiently computable counting queries, runs in time poly(d, n), and returns an approximate answer to each query, while satisfying differential privacy. We also consider the complexity of answering "simple" counting queries, and make some progress in this direction by showing that the above result holds even when we require that the queries are computable by constant depth (AC-0) circuits. #R##N#Our result is almost tight in the sense that nearly n^2 counting queries can be answered efficiently while satisfying differential privacy. Moreover, super-polynomially many queries can be answered in exponential time. #R##N#We prove our results by extending the connection between differentially private counting query release and cryptographic traitor-tracing schemes to the setting where the queries are given to the sanitizer as input, and by constructing a traitor-tracing scheme that is secure in this setting.

Connected through hyperedge HE-1 (induced by source paper "strong hardness of privacy from weak traitor tracing"):
  with members:
    - "order revealing encryption and the hardness of private learning"
    - "differentially private release and learning of threshold functions"
    - "between pure and approximate differential privacy"
    - "preventing false discovery in interactive data analysis is hard"
    - "private learning and sanitization pure vs approximate differential privacy"
    - "private multiplicative weights beyond linear queries"
    - "interactive fingerprinting codes and the hardness of preventing false discovery"
    - "faster private release of marginals on small databases"

Connected through hyperedge HE-2 (induced by source paper "concentrated differential privacy simplifications extensions and lower bounds"):
  with members:
    - "concentrated differential privacy"
    - "simultaneous private learning of multiple concepts"
    - "the complexity of computing the optimal composition of differential privacy"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "optimal sequential fingerprinting wald vs tardos"):
  with members:
    - "asymptotics of fingerprinting and group testing tight bounds from channel capacities"
    - "optimal symmetric tardos traitor tracing schemes"
    - "capacities and capacity achieving decoders for various fingerprinting games"
    - "interactive fingerprinting codes and the hardness of preventing false discovery"
    - "enhanced blind decoding of tardos codes with new map based functions"
    - "dynamic traitor tracing schemes revisited"
    - "discrete distributions in the tardos scheme revisited"
    - "fingerprinting codes and the price of approximate differential privacy"

Connected through hyperedge HE-4 (induced by source paper "order revealing encryption and the hardness of private learning"):
  with members:
    - "differentially private release and learning of threshold functions"
    - "the large margin mechanism for differentially private maximization"
    - "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"

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"
    - "optimal private halfspace counting via discrepancy"
    - "calibrating data to sensitivity in private data analysis"
    - "a learning theory approach to non interactive database privacy"
    - "iterative constructions and private data release"

Connected through hyperedge HE-6 (induced by source paper "algorithmic stability for adaptive data analysis"):
  with members:
    - "more general queries and less generalization error in adaptive data analysis"
    - "the ladder a reliable leaderboard for machine learning competitions"
    - "generalization in adaptive data analysis and holdout reuse"
    - "on the generalization properties of differential privacy"
    - "between pure and approximate differential privacy"
    - "preventing false discovery in interactive data analysis is hard"
    - "preserving statistical validity in adaptive data analysis"
    - "private multiplicative weights beyond linear queries"

Connected through hyperedge HE-7 (induced by source paper "private multiplicative weights beyond linear queries"):
  with members:
    - "more general queries and less generalization error in adaptive data analysis"
    - "preventing false discovery in interactive data analysis is hard"
    - "private learning and sanitization pure vs approximate differential privacy"
    - "preserving statistical validity in adaptive data analysis"
    - "faster private release of marginals on small databases"
    - "efficient algorithms for privately releasing marginals via convex relaxations"
    - "fingerprinting codes and the price of approximate differential privacy"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-8 (induced by source paper "dual query practical private query release for high dimensional data"):
  with members:
    - "characterizing the sample complexity of private learners"
    - "differential privacy for the analyst via private equilibrium computation"
    - "calibrating data to sensitivity in private data analysis"
    - "an adaptive mechanism for accurate query answering under differential privacy"
    - "faster algorithms for privately releasing marginals"
    - "accurate and efficient private release of datacubes and contingency tables"
    - "a learning theory approach to non interactive database privacy"
    - "iterative constructions and private data release"

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.CR(Cryptography and Security)