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: "federated heavy hitters discovery with differential privacy"
  Abstract: The discovery of heavy hitters (most frequent items) in user-generated data streams drives improvements in the app and web ecosystems, but often comes with substantial privacy risks. To address these risks, we propose a distributed and privacy-preserving algorithm for discovering the heavy hitters in a population of user-generated data streams. We critically leverage the sampling property of our distributed algorithm to prove that it is inherently differentially private, without requiring any addition of noise. We also examine the trade-off between privacy and utility, and show that our algorithm provides excellent utility while achieving strong privacy guarantees. We validate our findings both theoretically, using worst-case analyses, and practically, using a Twitter dataset with 1.6M tweets and over 650k users.

Connected through hyperedge HE-1 (induced by source paper "fedpaq a communication efficient federated learning method with periodic averaging and quantization"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "robust federated learning in a heterogeneous environment"
    - "one shot federated learning"
    - "matcha speeding up decentralized sgd via matching decomposition sampling"
    - "robust and communication efficient collaborative learning"
    - "peer to peer federated learning on graphs"
    - "federated learning challenges methods and future directions"

Connected through hyperedge HE-2 (induced by source paper "a federated learning approach for mobile packet classification"):
  with members:
    - "one shot federated learning"
    - "towards federated learning at scale system design"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "analyzing federated learning through an adversarial lens"
    - "exploiting unintended feature leakage in collaborative learning"
    - "federated learning strategies for improving communication efficiency"
    - "beyond inferring class representatives user level privacy leakage from federated learning"
    - "a hybrid approach to privacy preserving federated learning"

Connected through hyperedge HE-3 (induced by source paper "differential privacy has disparate impact on model accuracy"):
  with members:
    - "diversity in faces"
    - "fair decision making using privacy protected data"
    - "disparate vulnerability on the unfairness of privacy attacks against machine learning"
    - "class balanced loss based on effective number of samples"
    - "agnostic federated learning"
    - "a general approach to adding differential privacy to iterative training procedures"
    - "adversarial removal of demographic attributes from text data"
    - "differentially private fair learning"

Connected through hyperedge HE-4 (induced by source paper "privacy for free communication efficient learning with differential privacy using sketches"):
  with members:
    - "towards federated learning at scale system design"
    - "total stochastic gradient algorithms and applications in reinforcement learning"
    - "communication efficient distributed sgd with sketching"
    - "secure computation for machine learning with spdz"
    - "federated learning challenges methods and future directions"
    - "scalable and differentially private distributed aggregation in the shuffled model"
    - "enhancing the privacy of federated learning with sketching"
    - "differentially private meta learning"

Connected through hyperedge HE-5 (induced by source paper "context aware local differential privacy"):
  with members:
    - "communication complexity in locally private distribution estimation and heavy hitters"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "test without trust optimal locally private distribution testing"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "distributed differential privacy via shuffling"
    - "one sided differential privacy"
    - "practical locally private heavy hitters"
    - "optimal schemes for discrete distribution estimation under locally differential privacy"

Connected through hyperedge HE-6 (induced by source paper "federated learning of out of vocabulary words"):
  with members:
    - "applied federated learning improving google keyboard query suggestions"
    - "federated learning strategies for improving communication efficiency"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "federated learning for mobile keyboard prediction"
    - "lstm a search space odyssey"
    - "mobile keyboard input decoding with finite state transducers"
    - "conversational contextual cues the case of personalization and history for response ranking"
    - "practical secure aggregation for federated learning on user held data"

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)