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: "a survey on homomorphic encryption schemes theory and implementation"
  Abstract: Legacy encryption systems depend on sharing a key (public or private) among the peers involved in exchanging an encrypted message. However, this approach poses privacy concerns. Especially with popular cloud services, the control over the privacy of the sensitive data is lost. Even when the keys are not shared, the encrypted material is shared with a third party that does not necessarily need to access the content. Moreover, untrusted servers, providers, and cloud operators can keep identifying elements of users long after users end the relationship with the services. Indeed, Homomorphic Encryption (HE), a special kind of encryption scheme, can address these concerns as it allows any third party to operate on the encrypted data without decrypting it in advance. Although this extremely useful feature of the HE scheme has been known for over 30 years, the first plausible and achievable Fully Homomorphic Encryption (FHE) scheme, which allows any computable function to perform on the encrypted data, was introduced by Craig Gentry in 2009. Even though this was a major achievement, different implementations so far demonstrated that FHE still needs to be improved significantly to be practical on every platform. First, we present the basics of HE and the details of the well-known Partially Homomorphic Encryption (PHE) and Somewhat Homomorphic Encryption (SWHE), which are important pillars of achieving FHE. Then, the main FHE families, which have become the base for the other follow-up FHE schemes are presented. Furthermore, the implementations and recent improvements in Gentry-type FHE schemes are also surveyed. Finally, further research directions are discussed. This survey is intended to give a clear knowledge and foundation to researchers and practitioners interested in knowing, applying, as well as extending the state of the art HE, PHE, SWHE, and FHE systems.

Connected through hyperedge HE-1 (induced by source paper "short range audio channels security survey of mechanisms applications and research challenges"):
  with members:
    - "perils of zero interaction security in the internet of things"
    - "a survey on context based co presence detection techniques"
    - "doubleecho mitigating context manipulation attacks in copresence verification"
    - "survey and systematization of secure device pairing"
    - "continuous authentication for voice assistants"
    - "dolphinattack inaudible voice commands"
    - "don t skype type acoustic eavesdropping in voice over ip"
    - "sound proof usable two factor authentication based on ambient sound"

Connected through hyperedge HE-2 (induced by source paper "federated machine learning concept and applications"):
  with members:
    - "on device federated learning via blockchain and its latency analysis"
    - "exploiting unintended feature leakage in collaborative learning"
    - "federated learning strategies for improving communication efficiency"
    - "securing distributed machine learning in high dimensions"
    - "entity resolution and federated learning get a federated resolution"
    - "how to backdoor federated learning"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "federated meta learning for recommendation"

Connected through hyperedge HE-3 (induced by source paper "bayhenn combining bayesian deep learning and homomorphic encryption for secure dnn inference"):
  with members:
    - "tapas tricks to accelerate encrypted prediction as a service"
    - "gazelle a low latency framework for secure neural network inference"
    - "brain tumor segmentation with deep neural networks"
    - "ibm deep learning service"
    - "attention is all you need"
    - "efficient private erm for smooth objectives"
    - "a survey on deep learning in medical image analysis"
    - "deepsecure scalable provably secure deep learning"

Connected through hyperedge HE-4 (induced by source paper "a communication efficient vertical federated learning framework"):
  with members:
    - "federated machine learning concept and applications"
    - "federated optimization for heterogeneous networks"
    - "secure federated transfer learning"
    - "private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption"
    - "federated learning of deep networks using model averaging"

Connected through hyperedge HE-5 (induced by source paper "parallel distributed logistic regression for vertical federated learning without third party coordinator"):
  with members:
    - "towards federated learning at scale system design"
    - "federated machine learning concept and applications"
    - "private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption"
    - "federated optimization distributed machine learning for on device intelligence"

Connected through hyperedge HE-6 (induced by source paper "secure federated matrix factorization"):
  with members:
    - "federated machine learning concept and applications"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-7 (induced by source paper "rings an efficient java scala library for polynomial rings"):
  with members:
    - "form version 4 0"

Connected through hyperedge HE-8 (induced by source paper "ngraph he a graph compiler for deep learning on homomorphically encrypted data"):
  with members:
    - "private machine learning in tensorflow using secure computation"
    - "a generic framework for privacy preserving deep learning"
    - "a review of homomorphic encryption libraries for secure computation"
    - "chet compiler and runtime for homomorphic evaluation of tensor programs"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "tapas tricks to accelerate encrypted prediction as a service"
    - "gazelle a low latency framework for secure neural network inference"

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)