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 "nass optimizing secure inference via neural architecture search"):
  Abstract: Due to increasing privacy concerns, neural network (NN) based secure inference (SI) schemes that simultaneously hide the client inputs and server models attract major research interests. While existing works focused on developing secure protocols for NN-based SI, in this work, we take a different approach. We propose NASS, an integrated framework to search for tailored NN architectures designed specifically for SI. In particular, we propose to model cryptographic protocols as design elements with associated reward functions. The characterized models are then adopted in a joint optimization with predicted hyperparameters in identifying the best NN architectures that balance prediction accuracy and execution efficiency. In the experiment, it is demonstrated that we can achieve the best of both worlds by using NASS, where the prediction accuracy can be improved from 81.6% to 84.6%, while the inference runtime is reduced by 2x and communication bandwidth by 1.9x on the CIFAR-10 dataset.

Includes members:
  Member "video action recognition via neural architecture searching":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "searching central difference convolutional networks for face anti spoofing")
  Member "hardware software co exploration of neural architectures":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "co exploration of neural architectures and heterogeneous asic accelerator designs targeting multiple tasks")
  Member "accuracy vs efficiency achieving both through fpga implementation aware neural architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "low overhead online data flow tracking for intermittently powered non volatile fpgas")
  Member "auto deeplab hierarchical neural architecture search for semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "automl zero evolving machine learning algorithms from scratch")
  Member "on neural architecture search for resource constrained hardware platforms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "co exploration of neural architectures and heterogeneous asic accelerator designs targeting multiple tasks")
  Member "device circuit architecture co exploration for computing in memory neural accelerators":
    also connected through other hyperedges:
      (none)
  Member "xonn xnor based oblivious deep neural network inference":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ensei efficient secure inference via frequency domain homomorphic convolution for privacy preserving visual recognition")
  Member "prada protecting against dnn model stealing attacks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "model extraction attacks against recurrent neural networks")

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)