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: "pforest in network inference with random forests"
  Abstract: The concept of "self-driving networks" has recently emerged as a possible solution to manage the ever-growing complexity of modern network infrastructures. In a self-driving network, network devices adapt their decisions in real-time by observing network traffic and by performing in-line inference according to machine learning models. The recent advent of programmable data planes gives us a unique opportunity to implement this vision. One open question though is whether these devices are powerful enough to run such complex tasks? #R##N#We answer positively by presenting pForest, a system for performing in-network inference according to supervised machine learning models on top of programmable data planes. The key challenge is to design classification models that fit the constraints of programmable data planes (e.g., no floating points, no loops, and limited memory) while providing high accuracy. pForest addresses this challenge in three phases: (i) it optimizes the features selection according to the capabilities of programmable network devices; (ii) it trains random forest models tailored for different phases of a flow; and (iii) it applies these models in real time, on a per-packet basis. #R##N#We fully implemented pForest in Python (training), and in P4_16 (inference). Our evaluation shows that pForest can classify traffic at line rate for hundreds of thousands of flows, with an accuracy that is on-par with software-based solutions. We further show the practicality of pForest by deploying it on existing hardware devices (Barefoot Tofino).

Connected through hyperedge HE-1 (induced by source paper "explaining deep learning based networked systems"):
  with members:
    - "neural packet classification"
    - "hyper sagnn a self attention based graph neural network for hypergraphs"
    - "tensorflow js machine learning for the web and beyond"
    - "interpreting and improving natural language processing in machines with natural language processing in the brain"
    - "fooling neural network interpretations via adversarial model manipulation"
    - "gnn explainer a tool for post hoc explanation of graph neural networks"
    - "mmalfm explainable recommendation by leveraging reviews and images"
    - "unveiling the potential of graph neural networks for network modeling and optimization in sdn"

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)