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: "generalized optimization framework for graph based semi supervised learning"
  Abstract: We develop a generalized optimization framework for graph-based semi-supervised learning. The framework gives as particular cases the Standard Laplacian, Normalized Laplacian and PageRank based methods. We have also provided new probabilistic interpretation based on random walks and characterized the limiting behaviour of the methods. The random walk based interpretation allows us to explain di erences between the performances of methods with di erent smoothing kernels. It appears that the PageRank based method is robust with respect to the choice of the regularization parameter and the labelled data. We illustrate our theoretical results with two realistic datasets, characterizing di erent challenges: Les Miserables characters social network and Wikipedia hyper-link graph. The graph-based semi-supervised learning classi- es the Wikipedia articles with very good precision and perfect recall employing only the information about the hyper-text links.

Connected through hyperedge HE-1 (induced by source paper "certifiable robustness to graph perturbations"):
  with members:
    - "transferring robustness for graph neural network against poisoning attacks"
    - "adversarial examples on graph data deep insights into attack and defense"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "adversarial robustness of similarity based link prediction"
    - "adversarial defense framework for graph neural network"
    - "simplifying graph convolutional networks"
    - "learning graph neural networks with noisy labels"
    - "fast graph representation learning with pytorch geometric"

Connected through hyperedge HE-2 (induced by source paper "smoothing graph signals via random spanning forests"):
  with members:
    - "random sampling of bandlimited signals on graphs"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "chebyshev polynomial approximation for distributed signal processing"

Connected through hyperedge HE-3 (induced by source paper "l gamma pagerank for semi supervised learning"):
  with members:
    - "mean field analysis of personalized pagerank with implications for local graph clustering"
    - "phase transitions in semisupervised clustering of sparse networks"

Connected through hyperedge HE-4 (induced by source paper "the power of side information in subgraph detection"):
  with members:
    - "local set based graph signal reconstruction"
    - "signal recovery on graphs variation minimization"
    - "phase transitions in semisupervised clustering of sparse networks"
    - "phase transitions in spectral community detection"
    - "graph based anomaly detection and description a survey"
    - "bayesian discovery of threat networks"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-5 (induced by source paper "parallel and distributed approaches for graph based semi supervised learning"):
  with members:
    - "reinforcement learning for matrix computations pagerank as an example"
    - "community detection in the labelled stochastic block model"

Connected through hyperedge HE-6 (induced by source paper "personalized pagerank with node dependent restart"):
  with members:
    - "quick detection of nodes with large degrees"
    - "co authorship networks in the digital library research community"

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)