human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "how to backdoor federated learning"):
  Abstract: Federated learning enables multiple participants to jointly construct a deep learning model without sharing their private training data with each other. For example, multiple smartphones can jointly train a predictive keyboard model without revealing what individual users type into their phones. We demonstrate that any participant in federated learning can introduce hidden backdoor functionality into the joint global model, e.g., to ensure that an image classifier assigns an attacker-chosen label to images with certain features, or that a next-word predictor completes certain sentences with an attacker-chosen word. #R##N#We design and evaluate a new "constrain-and-scale" model-poisoning methodology and show that it greatly outperforms data poisoning. An attacker selected just once, in a single round of federated learning, can cause the global model to reach 100% accuracy on the backdoor task. We evaluate the attack under different assumptions and attack scenarios for standard federated learning tasks. We also show how to evade anomaly detection-based defenses by incorporating the evasion into the loss function when training the attack model.

Includes members:
  Member "strip a defence against trojan attacks on deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic backdoor attacks against machine learning models")
  Member "towards federated learning at scale system design":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "do we really need to access the source data source hypothesis transfer for unsupervised domain adaptation")
  Member "contamination attacks and mitigation in multi party machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the accuracy of influence functions for measuring group effects")
  Member "a little is enough circumventing defenses for distributed learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards byzantine resilient learning in decentralized systems")
  Member "distributed training with heterogeneous data bridging median and mean based algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "salvaging federated learning by local adaptation")
  Member "detecting backdoor attacks on deep neural networks by activation clustering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")
  Member "analyzing federated learning through an adversarial lens":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning home crowdsourced training of large neural networks using decentralized mixture of experts")
  Member "fine pruning defending against backdooring attacks on deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "entangled watermarks as a defense against model extraction")

Question: Please tell me which class the source paper 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)