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: "memristive threshold logic circuit design of fast moving object detection"
  Abstract: Real-time detection of moving objects involves memorization of features in the template image and their comparison with those in the test image. At high sampling rates, such techniques face the problems of high algorithmic complexity and component delays. We present a new resistive switching-based threshold logic  cell  which encodes the pixels of a template image. The cell comprises a voltage divider circuit that programs the resistances of the  memristors  arranged in a single-node threshold logic network, and the output is encoded as a binary value by using a CMOS inverter gate. When a test image is applied to the template-programmed cell, a mismatch in the respective pixels is seen as a change in the output voltage of the cell. The proposed cell when compared with CMOS equivalent implementation shows improved performance in area, leakage power, power dissipation, and delay.

Connected through hyperedge HE-1 (induced by source paper "practical implementation of memristor based threshold logic gates"):
  with members:
    - "programmable memristive threshold logic gate array"
    - "mixed precision in memory computing"
    - "hierarchical temporal memory using memristor networks a survey"
    - "approximate probabilistic neural networks with gated threshold logic"
    - "a survey of memristive threshold logic circuits"
    - "memristor threshold logic an overview to challenges and applications"
    - "a reconfigurable fir filter with memristor based weights"
    - "voltage controlled memristor threshold logic gates"

Connected through hyperedge HE-2 (induced by source paper "a survey of memristive threshold logic circuits"):
  with members:
    - "threshold logic computing memristive cmos circuits for fast fourier transform and vedic multiplication"
    - "design and synthesis of ultralow energy spin memristor threshold logic"
    - "resistive threshold logic"
    - "ultra low energy high performance dynamic resistive threshold logic"
    - "cognitive memory network"

Connected through hyperedge HE-3 (induced by source paper "voltage controlled memristor threshold logic gates"):
  with members:
    - "threshold logic computing memristive cmos circuits for fast fourier transform and vedic multiplication"
    - "resistive threshold logic"

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.CV(Computer Vision and Pattern Recognition)