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: "contamination attacks and mitigation in multi party machine learning"
  Abstract: Machine learning is data hungry; the more data a model has access to in training, the more likely it is to perform well at inference time. Distinct parties may want to combine their local data to gain the benefits of a model trained on a large corpus of data. We consider such a case: parties get access to the model trained on their joint data but do not see each others individual datasets. We show that one needs to be careful when using this multi-party model since a potentially malicious party can taint the model by providing contaminated data. We then show how adversarial training can defend against such attacks by preventing the model from learning trends specific to individual parties data, thereby also guaranteeing party-level membership privacy.

Connected through hyperedge HE-1 (induced by source paper "on the accuracy of influence functions for measuring group effects"):
  with members:
    - "towards efficient data valuation based on the shapley value"
    - "repairing without retraining avoiding disparate impact with counterfactual distributions"
    - "understanding the origins of bias in word embeddings"
    - "interpreting black box predictions using fisher kernels"
    - "certified defenses for data poisoning attacks"
    - "a broad coverage challenge corpus for sentence understanding through inference"

Connected through hyperedge HE-2 (induced by source paper "second order group influence functions for black box predictions"):
  with members:
    - "repairing without retraining avoiding disparate impact with counterfactual distributions"
    - "on the accuracy of influence functions for measuring group effects"
    - "machine learning in automated text categorization"

Connected through hyperedge HE-3 (induced by source paper "how to backdoor federated learning"):
  with members:
    - "strip a defence against trojan attacks on deep neural networks"
    - "towards federated learning at scale system design"
    - "a little is enough circumventing defenses for distributed learning"
    - "distributed training with heterogeneous data bridging median and mean based algorithms"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "analyzing federated learning through an adversarial lens"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "multi party poisoning through generalized p tampering"

Connected through hyperedge HE-4 (induced by source paper "reaching data confidentiality and model accountability on the caltrain"):
  with members:
    - "exploiting unintended feature leakage in collaborative learning"
    - "chiron privacy preserving machine learning as a service"
    - "how to backdoor federated learning"
    - "efficient deep learning on multi source private data"
    - "mitigating sybils in federated learning poisoning"
    - "securing input data of deep learning inference systems via partitioned enclave execution"
    - "the circle game scalable private membership test using trusted hardware"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"

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)