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: "deeppicar a low cost deep neural network based autonomous car"
  Abstract: We present DeepPicar, a low-cost deep neural network based autonomous car platform. DeepPicar is a small scale replication of a real self-driving car called DAVE-2 by NVIDIA. DAVE-2 uses a deep convolutional neural network (CNN), which takes images from a front-facing camera as input and produces car steering angles as output. DeepPicar uses the same network architecture---9 layers, 27 million connections and 250K parameters---and can drive itself in real-time using a web camera and a Raspberry Pi 3 quad-core platform. Using DeepPicar, we analyze the Pi 3's computing capabilities to support end-to-end deep learning based real-time control of autonomous vehicles. We also systematically compare other contemporary embedded computing platforms using the DeepPicar's CNN-based real-time control workload. We find that all tested platforms, including the Pi 3, are capable of supporting the CNN-based real-time control, from 20 Hz up to 100 Hz, depending on hardware platform. However, we find that shared resource contention remains an important issue that must be considered in applying CNN models on shared memory based embedded computing platforms; we observe up to 11.6X execution time increase in the CNN based control loop due to shared resource contention. To protect the CNN workload, we also evaluate state-of-the-art cache partitioning and memory bandwidth throttling techniques on the Pi 3. We find that cache partitioning is ineffective, while memory bandwidth throttling is an effective solution.

Connected through hyperedge HE-1 (induced by source paper "dynamic weighted simplex strategy for learning enabled cyber physical systems"):
  with members:
    - "attacking vision based perception in end to end autonomous driving models"
    - "vision based control of a quadrotor in user proximity mediated vs end to end learning approaches"
    - "reachable set computation and safety verification for neural networks with relu activations"
    - "an approach to reachability analysis for feed forward relu neural networks"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "a component based simplex architecture for high assurance cyber physical systems"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "deepdriving learning affordance for direct perception in autonomous driving"

Connected through hyperedge HE-2 (induced by source paper "approxnet content and contention aware video analytics system for the edge"):
  with members:
    - "focus querying large video datasets with low latency and low cost"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "blockdrop dynamic inference paths in residual networks"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "multi scale dense networks for resource efficient image classification"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-3 (induced by source paper "augmenting learning components for safety in resource constrained autonomous robots"):
  with members:
    - "reachable set computation and safety verification for neural networks with relu activations"
    - "limits of end to end learning"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "deeptest automated testing of deep neural network driven autonomous cars"
    - "a component based simplex architecture for high assurance cyber physical systems"
    - "end to end learning for self driving cars"
    - "reinforcement learning a survey"

Connected through hyperedge HE-4 (induced by source paper "rt gang real time gang scheduling framework for safety critical systems"):
  with members:
    - "denial of service attacks on shared cache in multicore analysis and prevention"
    - "protecting real time gpu kernels on integrated cpu gpu soc platforms"
    - "end to end learning for self driving cars"
    - "gang ftp scheduling of periodic and parallel rigid real time tasks"

Connected through hyperedge HE-5 (induced by source paper "virtual gang based scheduling of real time tasks on multicore platforms"):
  with members:
    - "rt gang real time gang scheduling framework for safety critical systems"
    - "denial of service attacks on shared cache in multicore analysis and prevention"
    - "gang ftp scheduling of periodic and parallel rigid real time tasks"

Connected through hyperedge HE-6 (induced by source paper "denial of service attacks on shared cache in multicore analysis and prevention"):
  with members:
    - "analysis of dynamic memory bandwidth regulation in multi core real time systems"
    - "parallelism aware memory interference delay analysis for cots multicore systems"

Connected through hyperedge HE-7 (induced by source paper "mpdcompress matrix permutation decomposition algorithm for deep neural network compression"):
  with members:
    - "shakeout a new approach to regularized deep neural network training"
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "structured deep neural network pruning via matrix pivoting"
    - "sep nets small and effective pattern networks"
    - "ese efficient speech recognition engine with sparse lstm on fpga"

Connected through hyperedge HE-8 (induced by source paper "a container based dos attack resilient control framework for real time uav systems"):
  with members:
    - "viconmavlink a software tool for indoor positioning using a motion capture system"
    - "seccomp towards practically defending against component hijacking in android applications"
    - "analysis of docker security"

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.OH(Other Computer Science)