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: "scnn a general distribution based statistical convolutional neural network with application to video object detection"
  Abstract: Various convolutional neural networks (CNNs) were developed recently that achieved accuracy comparable with that of human beings in computer vision tasks such as image recognition, object detection and tracking, etc. Most of these networks, however, process one single frame of image at a time, and may not fully utilize the temporal and contextual correlation typically present in multiple channels of the same image or adjacent frames from a video, thus limiting the achievable throughput. This limitation stems from the fact that existing CNNs operate on deterministic numbers. In this paper, we propose a novel statistical convolutional neural network (SCNN), which extends existing CNN architectures but operates directly on correlated distributions rather than deterministic numbers. By introducing a parameterized canonical model to model correlated data and defining corresponding operations as required for CNN training and inference, we show that SCNN can process multiple frames of correlated images effectively, hence achieving significant speedup over existing CNN models. We use a CNN based video object detection as an example to illustrate the usefulness of the proposed SCNN as a general network model. Experimental results show that even a non-optimized implementation of SCNN can still achieve 178% speedup over existing CNNs with slight accuracy degradation.

Connected through hyperedge HE-1 (induced by source paper "dlbc a deep learning based consensus in blockchains for deep learning services"):
  with members:
    - "energy recycling blockchain with proof of deep learning"
    - "exploiting computation power of blockchain for biomedical image segmentation"
    - "coin ai a proof of useful work scheme for blockchain based distributed deep learning"
    - "proof of search combining blockchain consensus formation with solving optimization problems"
    - "privacy aware cost effective scheduling considering non schedulable appliances in smart home"
    - "real time adversarial attacks"
    - "measuring human perception to improve handwritten document transcription"
    - "blockchain technology overview"

Connected through hyperedge HE-2 (induced by source paper "exploiting computation power of blockchain for biomedical image segmentation"):
  with members:
    - "energy recycling blockchain with proof of deep learning"
    - "blockchain technology overview"
    - "nnu net self adapting framework for u net based medical image segmentation"
    - "quantization of fully convolutional networks for accurate biomedical image segmentation"
    - "ct realistic lung nodule simulation from 3d conditional generative adversarial networks for robust lung segmentation"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "machine vision guided 3d medical image compression for efficient transmission and accurate segmentation in the clouds"):
  with members:
    - "accuracy vs efficiency achieving both through fpga implementation aware neural architecture search"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "dilated convolutional neural networks for cardiovascular mr segmentation in congenital heart disease"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "3d deeply supervised network for automatic liver segmentation from ct volumes"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "on neural architecture search for resource constrained hardware platforms"):
  with members:
    - "hardware software co exploration of neural architectures"
    - "accuracy vs efficiency achieving both through fpga implementation aware neural architecture search"
    - "exploiting computation power of blockchain for biomedical image segmentation"
    - "fbnet hardware aware efficient convnet design via differentiable neural architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "darts differentiable architecture search"
    - "mnasnet platform aware neural architecture search for mobile"
    - "learning transferable architectures for scalable image recognition"

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)