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: "beyond inferring class representatives user level privacy leakage from federated learning"
  Abstract: Federated learning, i.e., a mobile edge computing framework for deep learning, is a recent advance in privacy-preserving machine learning, where the model is trained in a decentralized manner by the clients, i.e., data curators, preventing the server from directly accessing those private data from the clients. This learning mechanism significantly challenges the attack from the server side. Although the state-of-the-art attacking techniques that incorporated the advance of Generative adversarial networks (GANs) could construct class representatives of the global data distribution among all clients, it is still challenging to distinguishably attack a specific client (i.e., user-level privacy leakage), which is a stronger privacy threat to precisely recover the private data from a specific client. This paper gives the first attempt to explore user-level privacy leakage against the federated learning by the attack from a malicious server. We propose a framework incorporating GAN with a multi-task discriminator, which simultaneously discriminates category, reality, and client identity of input samples. The novel discrimination on client identity enables the generator to recover user specified private data. Unlike existing works that tend to interfere the training process of the federated learning, the proposed method works "invisibly" on the server side. The experimental results demonstrate the effectiveness of the proposed attacking approach and the superior to the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "on the convergence of artificial intelligence and distributed ledger technology a scoping review and future research agenda"):
  with members:
    - "ekiden a platform for confidentiality preserving trustworthy and performant smart contracts"
    - "energy recycling blockchain with proof of deep learning"
    - "decentralized and collaborative ai on blockchain"
    - "sok transparent dishonesty front running attacks on blockchain"
    - "reliable federated learning for mobile networks"
    - "keystone an open framework for architecting tees"
    - "tensorscone a secure tensorflow framework using intel sgx"
    - "coin ai a proof of useful work scheme for blockchain based distributed deep learning"

Connected through hyperedge HE-2 (induced by source paper "the future of digital health with federated learning"):
  with members:
    - "multi site fmri analysis using privacy preserving federated learning and domain adaptation abide results"
    - "overcoming forgetting in federated learning on non iid data"
    - "advances and open problems in federated learning"
    - "deep leakage from gradients"
    - "privacy preserving federated brain tumour segmentation"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "peer to peer federated learning on graphs"
    - "p3sgd patient privacy preserving sgd for regularizing deep cnns in pathological image classification"

Connected through hyperedge HE-3 (induced by source paper "performance analysis and optimization in privacy preserving federated learning"):
  with members:
    - "advances and open problems in federated learning"
    - "on safeguarding privacy and security in the framework of federated learning"
    - "federated reinforcement learning"
    - "deep leakage from gradients"
    - "federated learning with differential privacy algorithms and performance analysis"
    - "the value of collaboration in convex machine learning with differential privacy"
    - "federated learning challenges methods and future directions"
    - "age based scheduling policy for federated learning in mobile edge networks"

Connected through hyperedge HE-4 (induced by source paper "data selection for federated learning with relevant and irrelevant data at clients"):
  with members:
    - "advances and open problems in federated learning"
    - "how does disagreement help generalization against label corruption"
    - "federated learning challenges methods and future directions"
    - "federated machine learning concept and applications"
    - "wireless network intelligence at the edge"
    - "leaf a benchmark for federated settings"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "adaptive federated learning in resource constrained edge computing systems"

Connected through hyperedge HE-5 (induced by source paper "anonymizing data for privacy preserving federated learning"):
  with members:
    - "differentially private learning with adaptive clipping"
    - "differential privacy enabled federated learning for sensitive health data"
    - "a hybrid approach to privacy preserving federated learning"
    - "adaptive federated learning in resource constrained edge computing systems"
    - "a general approach to adding differential privacy to iterative training procedures"
    - "how to backdoor federated learning"
    - "differentially private federated learning a client level perspective"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-6 (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"
    - "federated heavy hitters discovery with differential privacy"
    - "analyzing federated learning through an adversarial lens"
    - "exploiting unintended feature leakage in collaborative learning"
    - "federated learning strategies for improving communication efficiency"
    - "a hybrid approach to privacy preserving federated learning"

Connected through hyperedge HE-7 (induced by source paper "eavesdrop the composition proportion of training labels in federated learning"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "a little is enough circumventing defenses for distributed learning"
    - "updates leak data set inference and reconstruction attacks in online learning"
    - "federated learning for emoji prediction in a mobile keyboard"
    - "analyzing federated learning through an adversarial lens"
    - "exploiting unintended feature leakage in collaborative learning"
    - "multi party poisoning through generalized p tampering"
    - "federated learning for ultra reliable low latency v2v communications"

Connected through hyperedge HE-8 (induced by source paper "key protected classification for collaborative learning"):
  with members:
    - "pytorch an imperative style high performance deep learning library"
    - "exploiting unintended feature leakage in collaborative learning"
    - "adversarial attacks and defences a survey"
    - "privacy preserving machine learning through data obfuscation"
    - "differentially private generative adversarial network"
    - "efficient deep learning on multi source private data"
    - "personalized and private peer to peer machine learning"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"

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.LG(Machine Learning)