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: "semi supervised learning on graphs through reach and distance diffusion"
  Abstract: Semi-supervised learning (SSL) is an indispensable tool when there are few labeled entities and many unlabeled entities for which we want to predict labels. With graph-based methods, entities correspond to nodes in a graph and edges represent strong relations. At the heart of SSL algorithms is the specification of a dense {\em kernel} of pairwise affinity values from the graph structure. A learning algorithm is then trained on the kernel together with labeled entities. The most popular kernels are {\em spectral} and include the highly scalable "symmetric" Laplacian methods, that compute a soft labels using Jacobi iterations, and "asymmetric" methods including Personalized Page Rank (PPR) which use short random walks and apply with directed relations, such as like, follow, or hyperlinks. #R##N#We introduce {\em Reach diffusion} and {\em Distance diffusion} kernels that build on powerful social and economic models of centrality and influence in networks and capture the directed pairwise relations that underline social influence. Inspired by the success of social influence as an alternative to spectral centrality such as Page Rank, we explore SSL with our kernels and develop highly scalable algorithms for parameter setting, label learning, and sampling. We perform preliminary experiments that demonstrate the properties and potential of our kernels.

Connected through hyperedge HE-1 (induced by source paper "bootstrapped graph diffusions exposing the power of nonlinearity"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "revisiting semi supervised learning with graph embeddings"
    - "deep convolutional networks on graph structured data"
    - "large scale distributed semi supervised learning using streaming approximation"
    - "diffusion convolutional neural networks"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-2 (induced by source paper "semi supervised learning with competitive infection models"):
  with members:
    - "semi supervised classification with graph convolutional networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "deepwalk online learning of social representations"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "active semi supervised learning using sampling theory for graph signals"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "distributed representations of words and phrases and their compositionality"
    - "active semi supervised learning using submodular functions"

Connected through hyperedge HE-3 (induced by source paper "greedy maximization framework for graph based influence functions"):
  with members:
    - "reverse ranking by graph structure model and scalable algorithms"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "distance based influence in networks computation and maximization"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "trace complexity of network inference"
    - "uncovering the temporal dynamics of diffusion networks"

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.LG(Machine Learning)