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 vlsi design flow for secure side channel attack resistant ics"
  Abstract: This paper presents a digital VLSI design flow to create secure, side-channel attack (SCA) resistant integrated circuits. The design flow starts from a normal design in a hardware description language such as VHDL or Verilog and provides a direct path to a SCA resistant layout. Instead of a full custom layout or an iterative design process with extensive simulations, a few key modifications are incorporated in a regular synchronous CMOS standard cell design flow. We discuss the basis for side-channel attack resistance and adjust the library databases and constraints files of the synthesis and place & route procedures accordingly. Experimental results show that a DPA attack on a regular single ended CMOS standard cell implementation of a module of the DES algorithm discloses the secret key after 200 measurements. The same attack on a secure version still does not disclose the secret key after more than 2000 measurements.

Connected through hyperedge HE-1 (induced by source paper "pascal timing sca resistant design and verification flow"):
  with members:
    - "side channel attack resilience through route randomisation in secure real time networks on chip"

Connected through hyperedge HE-2 (induced by source paper "open dnn box by power side channel attack"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "finefool fine object contour attack via attention"
    - "i know what you see power side channel attack on convolutional neural network accelerators"
    - "csi neural network using side channels to recover your artificial neural network information"
    - "stealing neural networks via timing side channels"
    - "accelerator aware pruning for convolutional neural networks"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "nisp pruning networks using neuron importance score propagation"

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.AR(Hardware Architecture)