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: "sample complexity bounds on differentially private learning via communication complexity"
  Abstract: In this work we analyze the sample complexity of classification by differentially private algorithms. Differential privacy is a strong and well-studied notion of privacy introduced by Dwork et al. (2006) that ensures that the output of an algorithm leaks little information about the data point provided by any of the participating individuals. Sample complexity of private PAC and agnostic learning was studied in a number of prior works starting with (Kasiviswanathan et al., 2008) but a number of basic questions still remain open, most notably whether learning with privacy requires more samples than learning without privacy. #R##N#We show that the sample complexity of learning with (pure) differential privacy can be arbitrarily higher than the sample complexity of learning without the privacy constraint or the sample complexity of learning with approximate differential privacy. Our second contribution and the main tool is an equivalence between the sample complexity of (pure) differentially private learning of a concept class $C$ (or $SCDP(C)$) and the randomized one-way communication complexity of the evaluation problem for concepts from $C$. Using this equivalence we prove the following bounds: #R##N#1. $SCDP(C) = \Omega(LDim(C))$, where $LDim(C)$ is the Littlestone's (1987) dimension characterizing the number of mistakes in the online-mistake-bound learning model. Known bounds on $LDim(C)$ then imply that $SCDP(C)$ can be much higher than the VC-dimension of $C$. #R##N#2. For any $t$, there exists a class $C$ such that $LDim(C)=2$ but $SCDP(C) \geq t$. #R##N#3. For any $t$, there exists a class $C$ such that the sample complexity of (pure) $\alpha$-differentially private PAC learning is $\Omega(t/\alpha)$ but the sample complexity of the relaxed $(\alpha,\beta)$-differentially private PAC learning is $O(\log(1/\beta)/\alpha)$. This resolves an open problem of Beimel et al. (2013b).

Connected through hyperedge HE-1 (induced by source paper "an equivalence between private classification and online prediction"):
  with members:
    - "efficient noise tolerant and private learning via boosting"
    - "a necessary and sufficient stability notion for adaptive generalization"
    - "passing tests without memorizing two models for fooling discriminators"
    - "introduction to online convex optimization"
    - "privately learning thresholds closing the exponential gap"
    - "privately answering classification queries in the agnostic pac model"
    - "the role of interactivity in local differential privacy"
    - "private learning implies online learning an efficient reduction"

Connected through hyperedge HE-2 (induced by source paper "the power of synergy in differential privacy combining a small curator with local randomizers"):
  with members:
    - "locally private k means clustering"
    - "blender enabling local search with a hybrid differential privacy model"
    - "the privacy blanket of the shuffle model"
    - "differentially private summation with multi message shuffling"
    - "exponential separations in local differential privacy through communication complexity"
    - "scalable and differentially private distributed aggregation in the shuffled model"
    - "the role of interactivity in local differential privacy"
    - "amplification by shuffling from local to central differential privacy via anonymity"

Connected through hyperedge HE-3 (induced by source paper "private learning implies online learning an efficient reduction"):
  with members:
    - "passing tests without memorizing two models for fooling discriminators"
    - "efficient private algorithms for learning halfspaces"
    - "private pac learning implies finite littlestone dimension"
    - "learning in non convex games with an optimization oracle"
    - "how to use heuristics for differential privacy"
    - "calibrating noise to variance in adaptive data analysis"
    - "oracle efficient online learning and auction design"
    - "differentially private release and learning of threshold functions"

Connected through hyperedge HE-4 (induced by source paper "pac learning with stable and private predictions"):
  with members:
    - "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate"
    - "privately answering classification queries in the agnostic pac model"
    - "towards demystifying membership inference attacks"
    - "model agnostic private learning via stability"
    - "private pac learning implies finite littlestone dimension"
    - "privacy preserving prediction"
    - "towards measuring membership privacy"
    - "subgaussian tail bounds via stability arguments"

Connected through hyperedge HE-5 (induced by source paper "private center points and learning of halfspaces"):
  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"
    - "private learning and sanitization pure vs approximate differential privacy"
    - "learning privately with labeled and unlabeled examples"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-6 (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"
    - "private learning and sanitization pure vs approximate differential privacy"
    - "learning privately with labeled and unlabeled examples"
    - "differentially private combinatorial optimization"

Connected through hyperedge HE-7 (induced by source paper "privately learning thresholds closing the exponential gap"):
  with members:
    - "private pac learning implies finite littlestone dimension"
    - "differentially private release and learning of threshold functions"

Connected through hyperedge HE-8 (induced by source paper "the structure of optimal private tests for simple hypotheses"):
  with members:
    - "improved differentially private analysis of variance"
    - "privacy amplification by subsampling tight analyses via couplings and divergences"
    - "test without trust optimal locally private distribution testing"
    - "locally private hypothesis testing"
    - "inspectre privately estimating the unseen"
    - "generalization for adaptively chosen estimators via stable median"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "differentially private identity and closeness testing of discrete distributions"

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)