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: "graph2vec learning distributed representations of graphs"
  Abstract: Recent works on representation learning for graph structured data predominantly focus on learning distributed representations of graph substructures such as nodes and subgraphs. However, many graph analytics tasks such as graph classification and clustering require representing entire graphs as fixed length feature vectors. While the aforementioned approaches are naturally unequipped to learn such representations, graph kernels remain as the most effective way of obtaining them. However, these graph kernels use handcrafted features (e.g., shortest paths, graphlets, etc.) and hence are hampered by problems such as poor generalization. To address this limitation, in this work, we propose a neural embedding framework named graph2vec to learn data-driven distributed representations of arbitrary sized graphs. graph2vec's embeddings are learnt in an unsupervised manner and are task agnostic. Hence, they could be used for any downstream task such as graph classification, clustering and even seeding supervised representation learning approaches. Our experiments on several benchmark and large real-world datasets show that graph2vec achieves significant improvements in classification and clustering accuracies over substructure representation learning approaches and are competitive with state-of-the-art graph kernels.

Connected through hyperedge HE-1 (induced by source paper "few shot learning on graphs via super classes based on graph spectral measures"):
  with members:
    - "a survey on graph kernels"
    - "self attention graph pooling"
    - "a closer look at few shot classification"
    - "a comprehensive survey on graph neural networks"
    - "on first order meta learning algorithms"
    - "meta learning with latent embedding optimization"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-2 (induced by source paper "unsupervised hierarchical graph representation learning by mutual information maximization"):
  with members:
    - "fobe and hobe first and high order bipartite embeddings"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "large scale learnable graph convolutional networks"
    - "janossy pooling learning deep permutation invariant functions for variable size inputs"
    - "harp hierarchical representation learning for networks"
    - "neural message passing for quantum chemistry"
    - "predicting organic reaction outcomes with weisfeiler lehman network"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-3 (induced by source paper "segmented graph bert for graph instance modeling"):
  with members:
    - "graph bert only attention is needed for learning graph representations"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "dagcn dual attention graph convolutional networks"
    - "asap adaptive structure aware pooling for learning hierarchical graph representations"
    - "isonn isomorphic neural network for graph representation learning and classification"
    - "capsule neural networks for graph classification using explicit tensorial graph representations"
    - "gresnet graph residual network for reviving deep gnns from suspended animation"
    - "graph neural networks for small graph and giant network representation learning an overview"

Connected through hyperedge HE-4 (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"
    - "netlsd hearing the shape of a graph"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "a simple baseline algorithm for graph classification"

Connected through hyperedge HE-5 (induced by source paper "unicorn runtime provenance based detector for advanced persistent threats"):
  with members:
    - "poirot aligning attack behavior with kernel audit records for cyber threat hunting"
    - "runtime analysis of whole system provenance"
    - "holmes real time apt detection through correlation of suspicious information flows"
    - "sleuth real time attack scenario reconstruction from cots audit data"
    - "saql a stream based query system for real time abnormal system behavior detection"
    - "how powerful are graph neural networks"
    - "benchmarking crimes an emerging threat in systems security"
    - "frappuccino fault detection through runtime analysis of provenance"

Connected through hyperedge HE-6 (induced by source paper "learning distributed representations of graphs with geo2dr"):
  with members:
    - "on graph classification networks datasets and baselines"
    - "deep graph library towards efficient and scalable deep learning on graphs"
    - "fast graph representation learning with pytorch geometric"
    - "relational inductive biases deep learning and graph networks"
    - "graph embedding techniques applications and performance a survey"
    - "how powerful are graph neural networks"
    - "anonymous walk embeddings"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-7 (induced by source paper "geometric deep learning for computational mechanics part i anisotropic hyperelasticity"):
  with members:
    - "a physics constrained data driven approach based on locally convex reconstruction for noisy database"
    - "a comprehensive survey on graph neural networks"
    - "meta modeling game for deriving theory consistent microstructure based traction separation laws via deep reinforcement learning"
    - "a deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials"
    - "neural network with unbounded activation functions is universal approximator"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "the spectral underpinning of word2vec"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "multiview kernels for low dimensional modeling of seismic events"
    - "kernel scaling for manifold learning and classification"
    - "word2vec skip gram with negative sampling is a weighted logistic pca"
    - "node2vec scalable feature learning for networks"
    - "random walks on context spaces towards an explanation of the mysteries of semantic word embeddings"
    - "deepwalk online learning of social representations"
    - "distributed representations of sentences and documents"

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.AI(Artificial Intelligence)