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: "reducing the energy cost of inference via in sensor information processing"
  Abstract: There is much interest in incorporating inference capabilities into sensor-rich embedded platforms such as autonomous vehicles, wearables, and others. A central problem in the design of such systems is the need to extract information locally from sensed data on a severely limited energy budget. This necessitates the design of energy-efficient sensory embedded system. A typical sensory embedded system enforces a physical separation between sensing and computational subsystems - a separation mandated by the differing requirements of the sensing and computational functions. As a consequence, the energy consumption in such systems tends to be dominated by the energy consumed in transferring data over the sensor-processor interface (communication energy) and the energy consumed in processing the data in digital processor (computational energy). In this article, we propose an in-sensor computing architecture which (mostly) eliminates the sensor-processor interface by embedding inference computations in the noisy sensor fabric in analog and retraining the hyperparameters in order to compensate for non-ideal computations. The resulting architecture referred to as the Compute Sensor - a sensor that computes in addition to sensing - represents a radical departure from the conventional. We show that a Compute Sensor for image data can be designed by embedding both feature extraction and classification functions in the analog domain in close proximity to the CMOS active pixel sensor (APS) array. Significant gains in energy-efficiency are demonstrated using behavioral and energy models in a commercial semiconductor process technology. In the process, the Compute Sensor creates a unique opportunity to develop machine learning algorithms for information extraction from data on a noisy underlying computational fabric.

Connected through hyperedge HE-1 (induced by source paper "microprocessor optimizations for the internet of things a survey"):
  with members:
    - "phase distance mapping a phase based cache tuning methodology for embedded systems"
    - "context aware computing for the internet of things a survey"
    - "internet of things iot a vision architectural elements and future directions"

Connected through hyperedge HE-2 (induced by source paper "design automation for binarized neural networks a quantum leap opportunity"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "origami a 803 gop s w convolutional network accelerator"
    - "cbinfer change based inference for convolutional neural networks on video data"
    - "finn a framework for fast scalable binarized neural network inference"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "analysis and design of a passive switched capacitor matrix multiplier for approximate computing"
    - "deep learning with energy efficient binary gradient cameras"

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)