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: "differential privacy and the fat shattering dimension of linear queries"
  Abstract: In this paper, we consider the task of answering linear queries under the constraint of differential privacy. This is a general and well-studied class of queries that captures other commonly studied classes, including predicate queries and histogram queries. We show that the accuracy to which a set of linear queries can be answered is closely related to its fat-shattering dimension, a property that characterizes the learnability of real-valued functions in the agnostic-learning setting.

Connected through hyperedge HE-1 (induced by source paper "private learning and sanitization pure vs approximate differential privacy"):
  with members:
    - "characterizing the sample complexity of private learners"
    - "answering n 2 o 1 counting queries with differential privacy is hard"
    - "lower bounds in differential privacy"
    - "privately releasing conjunctions and the statistical query barrier"
    - "on the geometry of differential privacy"
    - "what can we learn privately"

Connected through hyperedge HE-2 (induced by source paper "fingerprinting codes and the price of approximate differential privacy"):
  with members:
    - "preventing false discovery in interactive data analysis is hard"
    - "characterizing the sample complexity of private learners"
    - "private learning and sanitization pure vs approximate differential privacy"
    - "space lower bounds for itemset frequency sketches"
    - "faster private release of marginals on small databases"
    - "the geometry of differential privacy the sparse and approximate cases"
    - "efficient algorithms for privately releasing marginals via convex relaxations"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-3 (induced by source paper "optimal private halfspace counting via discrepancy"):
  with members:
    - "calibrating data to sensitivity in private data analysis"
    - "a learning theory approach to non interactive database privacy"
    - "lower bounds in differential privacy"
    - "iterative constructions and private data release"
    - "a simple and practical algorithm for differentially private data release"
    - "privately releasing conjunctions and the statistical query barrier"
    - "differential privacy via wavelet transforms"
    - "on the geometry of differential privacy"

Connected through hyperedge HE-4 (induced by source paper "a learning theory approach to non interactive database privacy"):
  with members:
    - "calibrating data to sensitivity in private data analysis"
    - "private data release via learning thresholds"
    - "lower bounds in differential privacy"
    - "iterative constructions and private data release"
    - "a simple and practical algorithm for differentially private data release"
    - "privately releasing conjunctions and the statistical query barrier"
    - "differential privacy via wavelet transforms"
    - "on the geometry of 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.DS(Data Structures and Algorithms)