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 with density based distances"
  Abstract: We present a simple, yet effective, approach to Semi-Supervised Learning. Our approach is based on estimating density-based distances (DBD) using a shortest path calculation on a graph. These Graph-DBD estimates can then be used in any distance-based supervised learning method, such as Nearest Neighbor methods and SVMs with RBF kernels. In order to apply the method to very large data sets, we also present a novel algorithm which integrates nearest neighbor computations into the shortest path search and can find exact shortest paths even in extremely large dense graphs. Significant runtime improvement over the commonly used Laplacian regularization method is then shown on a large scale dataset.

Connected through hyperedge HE-1 (induced by source paper "power weighted shortest paths for unsupervised learning"):
  with members:
    - "cur decompositions similarity matrices and subspace clustering"
    - "exploration of a graph based density sensitive metric"
    - "computing the k nearest neighbors for all vertices via dijkstra"
    - "shortest path distance in random k nearest neighbor graphs"

Connected through hyperedge HE-2 (induced by source paper "semi supervised learning with competitive infection models"):
  with members:
    - "semi supervised learning on graphs through reach and distance diffusion"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "exploration of a graph based density sensitive metric"):
  with members:
    - "strong coresets for k median and subspace approximation goodbye dimension"
    - "adaptive metrics for adaptive samples"
    - "massively parallel algorithms and hardness for single linkage clustering under ell_p distances"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "approximating nearest neighbor distances"
    - "optimal euclidean spanners really short thin and lanky"
    - "efficient regression in metric spaces via approximate lipschitz extension"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-4 (induced by source paper "semi supervised learning on graphs through reach and distance diffusion"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "scalable facility location for massive graphs on pregel like systems"
    - "multi objective weighted sampling"
    - "reverse ranking by graph structure model and scalable algorithms"
    - "large scale distributed semi supervised learning using streaming approximation"
    - "deepwalk online learning of social representations"
    - "fast ppr scaling personalized pagerank estimation for large graphs"

Connected through hyperedge HE-5 (induced by source paper "a modular theory of feature learning"):
  with members:
    - "towards principled unsupervised learning"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"
    - "distributed representations of words and phrases and their compositionality"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-6 (induced by source paper "approximating nearest neighbor distances"):
  with members:
    - "a fast algorithm for well spaced points and approximate delaunay graphs"

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)