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: "ddgk learning graph representations for deep divergence graph kernels"
  Abstract: Can neural networks learn to compare graphs without feature engineering? In this paper, we show that it is possible to learn representations for graph similarity with neither domain knowledge nor supervision (i.e. feature engineering or labeled graphs). We propose Deep Divergence Graph Kernels, an unsupervised method for learning representations over graphs that encodes a relaxed notion of graph isomorphism. Our method consists of three parts. First, we learn an encoder for each anchor graph to capture its structure. Second, for each pair of graphs, we train a cross-graph attention network which uses the node representations of an anchor graph to reconstruct another graph. This approach, which we call isomorphism attention, captures how well the representations of one graph can encode another. We use the attention-augmented encoder's predictions to define a divergence score for each pair of graphs. Finally, we construct an embedding space for all graphs using these pair-wise divergence scores.    Unlike previous work, much of which relies on 1) supervision, 2) domain specific knowledge (e.g. a reliance on Weisfeiler-Lehman kernels), and 3) known node alignment, our unsupervised method jointly learns node representations, graph representations, and an attention-based alignment between graphs.    Our experimental results show that Deep Divergence Graph Kernels can learn an unsupervised alignment between graphs, and that the learned representations achieve competitive results when used as features on a number of challenging graph classification tasks. Furthermore, we illustrate how the learned attention allows insight into the the alignment of sub-structures across graphs.

Connected through hyperedge HE-1 (induced by source paper "just slaq when you approximate accurate spectral distances for web scale graphs"):
  with members:
    - "network density of states"
    - "netlsd hearing the shape of a graph"
    - "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-2 (induced by source paper "deep graph similarity learning a survey"):
  with members:
    - "simgnn a neural network approach to fast graph similarity computation"
    - "deep graph matching consensus"
    - "dynamic graph convolutional networks"
    - "glmnet graph learning matching networks for feature matching"
    - "from community to role based graph embeddings"
    - "learning combinatorial embedding networks for deep graph matching"
    - "community preserving graph convolutions for structural and functional joint embedding of brain networks"
    - "generalizing from a few examples a survey on few shot learning"

Connected through hyperedge HE-3 (induced by source paper "graph matching networks for learning the similarity of graph structured objects"):
  with members:
    - "a survey on graph kernels"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "deep sets"
    - "learning deep generative models of graphs"
    - "relational inductive biases deep learning and graph networks"
    - "dynamic graph cnn for learning on point clouds"
    - "how powerful are graph neural networks"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "is a single embedding enough learning node representations that capture multiple social contexts"):
  with members:
    - "enhanced network embeddings via exploiting edge labels"
    - "a tutorial on network embeddings"
    - "verse versatile graph embeddings from similarity measures"
    - "struc2vec learning node representations from structural identity"
    - "harp hierarchical representation learning for networks"
    - "learning edge representations via low rank asymmetric projections"
    - "watch your step learning node embeddings via graph attention"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-5 (induced by source paper "scalable global alignment graph kernel using random features from node embedding to graph embedding"):
  with members:
    - "revisiting random binning features fast convergence and strong parallelizability"
    - "scalable spectral clustering using random binning features"
    - "random warping series a random features method for time series embedding"
    - "sparse diffusion convolutional neural networks"
    - "dgcnn disordered graph convolutional neural network based on the gaussian mixture model"
    - "learning convolutional neural networks for graphs"
    - "primme_svds a high performance preconditioned svd solver for accurate large scale computations"
    - "on valid optimal assignment kernels and applications to graph classification"

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)