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: "smart surveillance as an edge network service from harr cascade svm to a lightweight cnn"
  Abstract: Edge computing efficiently extends the realm of information technology beyond the boundary defined by cloud computing paradigm. Performing computation near the source and destination, edge computing is promising to address the challenges in many delay-sensitive applications, like real-time human surveillance. Leveraging the ubiquitously connected cameras and smart mobile devices, it enables video analytics at the edge. In recent years, many smart video surveillance approaches are proposed for object detection and tracking by using Artificial Intelligence (AI) and Machine Learning (ML) algorithms. This work explores the feasibility of two popular human-objects detection schemes, Harr-Cascade and HOG feature extraction and SVM classifier, at the edge and introduces a lightweight Convolutional Neural Network (L-CNN) leveraging the depthwise separable convolution for less computation, for human detection. Single Board computers (SBC) are used as edge devices for tests and algorithms are validated using real-world campus surveillance video streams and open data sets. The experimental results are promising that the final algorithm is able to track humans with a decent accuracy at a resource consumption affordable by edge devices in real-time manner.

Connected through hyperedge HE-1 (induced by source paper "machine learning at the network edge a survey"):
  with members:
    - "energy efficient radio resource allocation for federated edge learning"
    - "network offloading policies for cloud robotics a learning based approach"
    - "deep learning at the edge"
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "edgelens deep learning based object detection in integrated iot fog and cloud computing environments"
    - "dabnn a super fast inference framework for binary neural networks on arm devices"
    - "pcamp performance comparison of machine learning packages on the edges"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"

Connected through hyperedge HE-2 (induced by source paper "elfish resource aware federated learning on heterogeneous edge devices"):
  with members:
    - "safa a semi asynchronous protocol for fast federated learning with low overhead"
    - "distributed learning of deep neural networks using independent subnet training"
    - "towards federated learning at scale system design"
    - "federated learning challenges methods and future directions"
    - "asynchronous online federated learning for edge devices"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "federated learning strategies for improving communication efficiency"
    - "adaptive federated learning in resource constrained edge computing systems"

Connected through hyperedge HE-3 (induced by source paper "decentralized smart surveillance through microservices platform"):
  with members:
    - "a study on smart online frame forging attacks against video surveillance system"
    - "blendmas a blockchain enabled decentralized microservices architecture for smart public safety"
    - "fog computing a taxonomy survey and future directions"
    - "a microservice enabled architecture for smart surveillance using blockchain technology"
    - "an exploration of blockchain enabled decentralized capability based access control strategy for space situation awareness"
    - "kerman a hybrid lightweight tracking algorithm to enable smart surveillance as an edge service"
    - "constructing trustworthy and safe communities on a blockchain enabled social credits system"
    - "real time index authentication for event oriented surveillance video query using blockchain"

Connected through hyperedge HE-4 (induced by source paper "blockchain methods for trusted avionics systems"):
  with members:
    - "decentralized smart surveillance through microservices platform"
    - "a microservice enabled architecture for smart surveillance using blockchain technology"
    - "kerman a hybrid lightweight tracking algorithm to enable smart surveillance as an edge service"
    - "constructing trustworthy and safe communities on a blockchain enabled social credits system"
    - "real time index authentication for event oriented surveillance video query using blockchain"
    - "real time human detection as an edge service enabled by a lightweight cnn"
    - "eiqis toward an event oriented indexable and queryable intelligent surveillance system"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-5 (induced by source paper "no peeking through my windows conserving privacy in personal drones"):
  with members:
    - "yolov3 an incremental improvement"
    - "unmanned aerial vehicle forensic investigation process dji phantom 3 drone as a case study"
    - "privacy preserving machine learning threats and solutions"
    - "internet of drones"
    - "ssd single shot multibox detector"
    - "wireless communications with unmanned aerial vehicles opportunities and challenges"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"

Connected through hyperedge HE-6 (induced by source paper "blendmas a blockchain enabled decentralized microservices architecture for smart public safety"):
  with members:
    - "decentralized smart surveillance through microservices platform"
    - "a microservice enabled architecture for smart surveillance using blockchain technology"
    - "kerman a hybrid lightweight tracking algorithm to enable smart surveillance as an edge service"
    - "constructing trustworthy and safe communities on a blockchain enabled social credits system"
    - "real time index authentication for event oriented surveillance video query using blockchain"
    - "real time human detection as an edge service enabled by a lightweight cnn"
    - "eiqis toward an event oriented indexable and queryable intelligent surveillance system"

Connected through hyperedge HE-7 (induced by source paper "a study on smart online frame forging attacks against video surveillance system"):
  with members:
    - "internet of things survey on security"
    - "a microservice enabled architecture for smart surveillance using blockchain technology"
    - "real time human detection as an edge service enabled by a lightweight cnn"
    - "eiqis toward an event oriented indexable and queryable intelligent surveillance system"

Connected through hyperedge HE-8 (induced by source paper "kerman a hybrid lightweight tracking algorithm to enable smart surveillance as an edge service"):
  with members:
    - "real time human detection as an edge service enabled by a lightweight cnn"
    - "an edge fog cloud platform for anticipatory learning process designed for internet of mobile things"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "need for speed a benchmark for higher frame rate object tracking"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "high speed tracking with kernelized correlation filters"
    - "correlation filters with limited boundaries"

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)