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: "draining our glass an energy and heat characterization of google glass"
  Abstract: The Google Glass is a mobile device designed to be worn as eyeglasses. This form factor enables new usage possibilities, such as hands-free video chats and instant web search. However, its shape also hampers its potential: (1) battery size, and therefore lifetime, is limited by a need for the device to be lightweight, and (2) high-power processing leads to significant heat, which should be limited, due to the Glass' compact form factor and close proximity to the user's skin. We use the Glass in a case study of the power and thermal characteristics of optical head-mounted display devices. We share insights and implications to limit power consumption to increase the safety and utility of head-mounted devices.

Connected through hyperedge HE-1 (induced by source paper "ecc platform independent energy constrained deep neural network compression via a bilinear regression model"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "euphrates algorithm soc co design for low power mobile continuous vision"
    - "amc automl for model compression and acceleration on mobile devices"
    - "energy constrained compression for deep neural networks via weighted sparse projection and layer input masking"
    - "a unified framework of dnn weight pruning and weight clustering quantization using admm"
    - "compressing neural networks using the variational information bottleneck"
    - "learning efficient convolutional networks through network slimming"
    - "self normalizing neural networks"

Connected through hyperedge HE-2 (induced by source paper "euphrates algorithm soc co design for low power mobile continuous vision"):
  with members:
    - "mobile machine learning hardware at arm a systems on chip soc perspective"
    - "in datacenter performance analysis of a tensor processing unit"
    - "fast yolo a fast you only look once system for real time embedded object detection in video"
    - "towards closing the energy gap between hog and cnn features for embedded vision"
    - "video classification with cnns using the codec as a spatio temporal activity sensor"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "reconfiguring the imaging pipeline for computer vision"

Connected through hyperedge HE-3 (induced by source paper "cs vqa visual question answering with compressively sensed images"):
  with members:
    - "fast integral image estimation at 1 measurement rate"
    - "deep fully connected networks for video compressive sensing"
    - "hierarchical question image co attention for visual question answering"
    - "making the v in vqa matter elevating the role of image understanding in visual question answering"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "a deep learning approach to structured signal recovery"
    - "deep residual learning for image recognition"
    - "reconstruction free action inference from compressive imagers"

Connected through hyperedge HE-4 (induced by source paper "virtualized application function chaining maximizing the wearable system lifetime"):
  with members:
    - "specifying and placing chains of virtual network functions"

Connected through hyperedge HE-5 (induced by source paper "minimum energy quantized neural networks"):
  with members:
    - "large scale evolution of image classifiers"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "ristretto hardware oriented approximation of convolutional neural networks"
    - "eie efficient inference engine on compressed deep neural network"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "trained ternary quantization"
    - "energy efficient convnets through approximate computing"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "exploring computation communication tradeoffs in camera systems"):
  with members:
    - "asp vision optically computing the first layer of convolutional neural networks using angle sensitive pixels"

Connected through hyperedge HE-7 (induced by source paper "deep learning with energy efficient binary gradient cameras"):
  with members:
    - "image restoration using convolutional auto encoders with symmetric skip connections"
    - "eie efficient inference engine on compressed deep neural network"
    - "asp vision optically computing the first layer of convolutional neural networks using angle sensitive pixels"
    - "wider face a face detection benchmark"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "gesture based continuous authentication for wearable devices the google glass case"):
  with members:
    - "touchalytics on the applicability of touchscreen input as a behavioral biometric for continuous authentication"

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.HC(Human-Computer Interaction)