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: "graph filtration learning"
  Abstract: We propose an approach to learning with graph-structured data in the problem domain of graph classification. In particular, we present a novel type of readout operation to aggregate node features into a graph-level representation. To this end, we leverage persistent homology computed via a real-valued, learnable, filter function. We establish the theoretical foundation for differentiating through the persistent homology computation. Empirically, we show that this type of readout operation compares favorably to previous techniques, especially when the graph connectivity structure is informative for the learning problem.

Connected through hyperedge HE-1 (induced by source paper "deep graph mapper seeing graphs through the neural lens"):
  with members:
    - "mincut pooling in graph neural networks"
    - "asap adaptive structure aware pooling for learning hierarchical graph representations"
    - "graph convolutional networks with eigenpooling"
    - "clique pooling for graph classification"
    - "graph u nets"
    - "on graph classification networks datasets and baselines"
    - "self attention graph pooling"
    - "gnnexplainer generating explanations for graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "universal function approximation on graphs using multivalued functions"):
  with members:
    - "a survey on graph kernels"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "deep learning on graphs a survey"
    - "how powerful are graph neural networks"
    - "neural network with unbounded activation functions is universal approximator"
    - "understanding deep neural networks with rectified linear units"
    - "graph isomorphism in quasipolynomial time"
    - "diffusion convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "topological autoencoders"):
  with members:
    - "neural persistence a complexity measure for deep neural networks using algebraic topology"
    - "homology preserving dimensionality reduction via manifold landmarking and tearing"
    - "on characterizing the capacity of neural networks using algebraic topology"
    - "improved topological approximations by digitization"
    - "adam a method for stochastic optimization"
    - "linear size approximations to the vietoris rips filtration"

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)