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 "enabling privacy preserving compute and data intensive computing using heterogeneous trusted execution environment"):
  Abstract: There is an urgent demand for privacy-preserving techniques capable of supporting compute and data intensive (CDI) computing in the era of big data. However, none of existing TEEs can truly support CDI computing tasks, as CDI requires high throughput accelerators like GPU and TPU but TEEs do not offer security protection of such accelerators. This paper present HETEE (Heterogeneous TEE), the first design of TEE capable of strongly protecting heterogeneous computing with unsecure accelerators. HETEE is uniquely constructed to work with today's servers, and does not require any changes for existing commercial CPUs or accelerators. The key idea of our design runs security controller as a stand-alone computing system to dynamically adjust the boundary of between secure and insecure worlds through the PCIe switches, rendering the control of an accelerator to the host OS when it is not needed for secure computing, and shifting it back when it is. The controller is the only trust unit in the system and it runs the custom OS and accelerator runtimes, together with the encryption, authentication and remote attestation components. The host server and other computing systems communicate with controller through an in memory task queue that accommodates the computing tasks offloaded to HETEE, in the form of encrypted and signed code and data. Also, HETEE offers a generic and efficient programming model to the host CPU. We have implemented the HETEE design on a hardware prototype system, and evaluated it with large-scale Neural Networks inference and training tasks. Our evaluations show that HETEE can easily support such secure computing tasks and only incurs a 12.34% throughput overhead for inference and 9.87% overhead for training on average.

Includes members:
  Member "a survey of techniques for improving security of gpus":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hacking neural networks a short introduction")
  Member "sgxio generic trusted i o path for intel sgx":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "secloak arm trustzone based mobile peripheral control")
  Member "vulnerable gpu memory management towards recovering raw data from gpu":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey of techniques for improving security of gpus")
  Member "cuda leaks a detailed hack for cuda and a partial fix":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "virtualization technologies and cloud security advantages issues and perspectives")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")

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)