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: "optimal virtual network function placement in multi cloud service function chaining architecture"
  Abstract: Service Function Chaining (SFC) is the problem of deploying various network service instances over geographically distributed data centers and providing inter-connectivity among them. The goal is to enable the network traffic to flow smoothly through the underlying network, resulting in an optimal quality of experience to the end-users. Proper chaining of network functions leads to optimal utilization of distributed resources. This has been a de-facto model in the telecom industry with network functions deployed over underlying hardware. Though this model has served the telecom industry well so far, it has been adapted mostly to suit the static behavior of network services and service demands due to the deployment of the services directly over physical resources. This results in network ossification with larger delays to the end-users, especially with the data-centric model in which the computational resources are moving closer to end users. A novel networking paradigm, Network Function Virtualization (NFV), meets the user demands dynamically and reduces operational expenses (OpEx) and capital expenditures (CapEx), by implementing network functions in the software layer known as virtual network functions (VNFs). VNFs are then interconnected to form a complete end-to-end service, also known as service function chains (SFCs). In this work, we study the problem of deploying service function chains over network function virtualized architecture. Specifically, we study virtual network function placement problem for the optimal SFC formation across geographically distributed clouds. We set up the problem of minimizing inter-cloud traffic and response time in a multi-cloud scenario as an ILP optimization problem, along with important constraints such as total deployment costs and service level agreements (SLAs). We consider link delays and computational delays in our model. The link queues are modeled as M/D/1 (single server/Poisson arrival/deterministic service times) and server queues as M/M/1 (single server/Poisson arrival/exponential service times) based on the statistical analysis. In addition, we present a novel affinity-based approach (ABA) to solve the problem for larger networks. We provide a performance comparison between the proposed heuristic and simple greedy approach (SGA) used in the state-of-the-art systems. Greedy approach has already been widely studied in the literature for the VM placement problem. Especially we compare our proposed heuristic with a greedy approach using first-fit decreasing (FFD) method. By observing the results, we conclude that the affinity-based approach for placing the service functions in the network produces better results compared against the simple greedy (FFD) approach in terms of both, total delays and total resource cost. We observe that with a little compromise (gap of less than 10% of the optimal) in the solution quality (total delays and cost), affinity-based heuristic can solve the larger problem more quickly than ILP.

Connected through hyperedge HE-1 (induced by source paper "cybersecurity for industrial control systems a survey"):
  with members:
    - "machine learning based network vulnerability analysis of industrial internet of things"
    - "effect of imbalanced datasets on security of industrial iot using machine learning"
    - "machine learning for anomaly detection and categorization in multi cloud environments"
    - "feasibility of supervised machine learning for cloud security"

Connected through hyperedge HE-2 (induced by source paper "hierarchical multi agent optimization for resource allocation in cloud computing"):
  with members:
    - "boosting cooperative coevolution for large scale optimization with a fine grained computation resource allocation strategy"

Connected through hyperedge HE-3 (induced by source paper "joint reliability aware and cost efficient path allocation and vnf placement using sharing scheme"):
  with members:
    - "implementation of virtual network function chaining through segment routing in a linux based nfv infrastructure"
    - "on the trade off between computational load and reliability for network function virtualization"
    - "distributed service function chaining"
    - "an approximation algorithm for path computation and function placement in sdns"
    - "service chain and virtual network embeddings approximations using randomized rounding"

Connected through hyperedge HE-4 (induced by source paper "reducing service deployment cost through vnf sharing"):
  with members:
    - "getting the most out of your vnfs flexible assignment of service priorities in 5g"
    - "resource orchestration of 5g transport networks for vertical industries"
    - "arbitration among vertical services"
    - "network slicing based 5g and future mobile networks mobility resource management and challenges"
    - "network slicing to enable scalability and flexibility in 5g mobile networks"

Connected through hyperedge HE-5 (induced by source paper "getting the most out of your vnfs flexible assignment of service priorities in 5g"):
  with members:
    - "resource orchestration of 5g transport networks for vertical industries"
    - "arbitration among vertical services"
    - "network slicing based 5g and future mobile networks mobility resource management and challenges"
    - "network slicing to enable scalability and flexibility in 5g mobile networks"

Connected through hyperedge HE-6 (induced by source paper "vnf placement and resource allocation for the support of vertical services in 5g networks"):
  with members:
    - "arbitration among vertical services"
    - "network slicing based 5g and future mobile networks mobility resource management and challenges"
    - "modeling and dimensioning of a virtualized mme for 5g mobile networks"
    - "network slicing to enable scalability and flexibility in 5g mobile networks"

Connected through hyperedge HE-7 (induced by source paper "joint failure recovery fault prevention and energy efficient resource management for real time sfc in fog supported sdn"):
  with members:
    - "implementation of virtual network function chaining through segment routing in a linux based nfv infrastructure"
    - "distributed service function chaining"
    - "joint energy efficient and qos aware path allocation and vnf placement for service function chaining"
    - "an approximation algorithm for path computation and function placement in sdns"
    - "fog computing based radio access networks issues and challenges"
    - "service chain and virtual network embeddings approximations using randomized rounding"
    - "fog computing focusing on mobile users at the edge"

Connected through hyperedge HE-8 (induced by source paper "exploring microservices for enhancing internet qos"):
  with members:
    - "an nfv and microservice based architecture for on the fly component provisioning in content delivery networks"
    - "about microservices containers and their underestimated impact on network performance"
    - "circuit breakers discovery and api gateways in microservices"
    - "virtual machine placement literature review"
    - "migrating to cloud native architectures using microservices an experience report"
    - "specifying and placing chains of virtual network functions"

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.NI(Networking and Internet Architecture)