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: "netlsd hearing the shape of a graph"
  Abstract: Comparison among graphs is ubiquitous in graph analytics. However, it is a hard task in terms of the expressiveness of the employed similarity measure and the efficiency of its computation. Ideally, graph comparison should be invariant to the order of nodes and the sizes of compared graphs, adaptive to the scale of graph patterns, and scalable. Unfortunately, these properties have not been addressed together. Graph comparisons still rely on direct approaches, graph kernels, or representation-based methods, which are all inefficient and impractical for large graph collections. In this paper, we propose the Network Laplacian Spectral Descriptor (NetLSD): the first, to our knowledge, permutation- and size-invariant, scale-adaptive, and efficiently computable graph representation method that allows for straightforward comparisons of large graphs. NetLSD extracts a compact signature that inherits the formal properties of the Laplacian spectrum, specifically its heat or wave kernel; thus, it \em hears the shape of a graph. Our evaluation on a variety of real-world graphs demonstrates that it outperforms previous works in both expressiveness and efficiency.

Connected through hyperedge HE-1 (induced by source paper "an api oriented open source python framework for unsupervised learning on graphs"):
  with members:
    - "fast sequence based embedding with diffusion graphs"
    - "multi scale attributed node embedding"
    - "scipy 1 0 fundamental algorithms for scientific computing in python"
    - "edmot an edge enhancement approach for motif aware community detection"
    - "pytorch an imperative style high performance deep learning library"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "a simple baseline algorithm for graph classification"
    - "learning structural node embeddings via diffusion wavelets"

Connected through hyperedge HE-2 (induced by source paper "copt coordinated optimal transport on graphs"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "graph coarsening with preserved spectral properties"
    - "a unifying framework for spectrum preserving graph sparsification and coarsening"
    - "got an optimal transport framework for graph comparison"
    - "solving graph compression via optimal transport"
    - "gromov wasserstein alignment of word embedding spaces"
    - "approximate nearest neighbor search in high dimensions"
    - "graph reduction with spectral and cut guarantees"

Connected through hyperedge HE-3 (induced by source paper "estimating descriptors for large graphs"):
  with members:
    - "fleet butterfly estimation from a bipartite graph stream"
    - "stochastic graphlet embedding"
    - "a comprehensive survey on graph neural networks"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "how powerful are graph neural networks"
    - "wrs waiting room sampling for accurate triangle counting in real graph streams"
    - "graph isomorphism in quasipolynomial time"
    - "deltacon a principled massive graph similarity function"

Connected through hyperedge HE-4 (induced by source paper "wasserstein based graph alignment"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "on the convergence of adam and beyond"
    - "learning permutations with sinkhorn policy gradient"
    - "ds a flexible scalable and provably tight relaxation for matching problems"
    - "improved bilinear pooling with cnns"
    - "robust principal component analysis on graphs"
    - "learning laplacian matrix in smooth graph signal representations"

Connected through hyperedge HE-5 (induced by source paper "just slaq when you approximate accurate spectral distances for web scale graphs"):
  with members:
    - "network density of states"
    - "ddgk learning graph representations for deep divergence graph kernels"
    - "regal representation learning based graph alignment"
    - "a simple yet effective baseline for non attributed graph classification"
    - "approximating the spectrum of a graph"
    - "deltacon a principled massive graph similarity function"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-6 (induced by source paper "ddgk learning graph representations for deep divergence graph kernels"):
  with members:
    - "is a single embedding enough learning node representations that capture multiple social contexts"
    - "hierarchical graph representation learning with differentiable pooling"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "a tutorial on network embeddings"
    - "graph edit distance computation via graph neural networks"
    - "verse versatile graph embeddings from similarity measures"
    - "relational inductive biases deep learning and graph networks"
    - "on the dimensionality of word embedding"

Connected through hyperedge HE-7 (induced by source paper "graph coarsening with preserved spectral properties"):
  with members:
    - "network density of states"
    - "a unifying framework for spectrum preserving graph sparsification and coarsening"
    - "fully dynamic spectral vertex sparsifiers and applications"
    - "solving graph compression via optimal transport"
    - "graph reduction with spectral and cut guarantees"
    - "spectrally approximating large graphs with smaller graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "discriminative embeddings of latent variable models for structured data"

Connected through hyperedge HE-8 (induced by source paper "got an optimal transport framework for graph comparison"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "on the convergence of adam and beyond"
    - "gromov wasserstein alignment of word embedding spaces"
    - "implicit reparameterization gradients"
    - "learning permutations with sinkhorn policy gradient"
    - "ds a flexible scalable and provably tight relaxation for matching problems"
    - "improved bilinear pooling with cnns"
    - "network topology inference from spectral templates"

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.SI(Social and Information Networks)