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: "fast warped graph embedding unifying framework and one click algorithm"
  Abstract: What is the best way to describe a user in a social network with just a few numbers? Mathematically, this is equivalent to assigning a vector representation to each node in a graph, a process called graph embedding. We propose a novel framework, GEM-D that unifies most of the past algorithms such as LapEigs, DeepWalk and node2vec. GEM-D achieves its goal by decomposing any graph embedding algorithm into three building blocks: node proximity function, warping function and loss function. Based on thorough analysis of GEM-D, we propose a novel algorithm, called UltimateWalk, which outperforms the most-recently proposed state-of-the-art DeepWalk and node2vec. The contributions of this work are: (1) The proposed framework, GEM-D unifies the past graph embedding algorithms and provides a general recipe of how to design a graph embedding; (2) the nonlinearlity in the warping function contributes significantly to the quality of embedding and the exponential function is empirically optimal; (3) the proposed algorithm, UltimateWalk is one-click (no user-defined parameters), scalable and has a closed-form solution.

Connected through hyperedge HE-1 (induced by source paper "improving textual network learning with variational homophilic embeddings"):
  with members:
    - "topic guided variational autoencoders for text generation"
    - "pytorch biggraph a large scale graph embedding system"
    - "network structure from rich but noisy data"
    - "understanding convolutional neural networks for text classification"
    - "constrained graph variational autoencoders for molecule design"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "interpretable vaes for nonlinear group factor analysis"
    - "nash toward end to end neural architecture for generative semantic hashing"

Connected through hyperedge HE-2 (induced by source paper "scalable graph embeddings via sparse transpose proximities"):
  with members:
    - "fast randomized pca for sparse data"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "verse versatile graph embeddings from similarity measures"
    - "graph embedding techniques applications and performance a survey"
    - "network representation learning a survey"
    - "struc2vec learning node representations from structural identity"
    - "a survey on network embedding"
    - "sne signed network embedding"

Connected through hyperedge HE-3 (induced by source paper "fast and accurate network embeddings via very sparse random projection"):
  with members:
    - "netsmf large scale network embedding as sparse matrix factorization"
    - "a comparative study for unsupervised network representation learning"
    - "a comprehensive comparison of unsupervised network representation learning methods"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "verse versatile graph embeddings from similarity measures"
    - "billion scale network embedding with iterative random projection"
    - "node2vec scalable feature learning for networks"
    - "pte predictive text embedding through large scale heterogeneous text networks"

Connected through hyperedge HE-4 (induced by source paper "glee geometric laplacian eigenmap embedding"):
  with members:
    - "the simplex geometry of graphs"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "graph embedding techniques applications and performance a survey"
    - "representation learning on graphs methods and applications"
    - "node2vec scalable feature learning for networks"
    - "robustness and algebraic connectivity of random interdependent networks"
    - "deepwalk online learning of social representations"
    - "graph sparsification by effective resistances"

Connected through hyperedge HE-5 (induced by source paper "geometric laplacian eigenmap embedding"):
  with members:
    - "the simplex geometry of graphs"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "a tutorial on network embeddings"
    - "representation learning on graphs methods and applications"
    - "node2vec scalable feature learning for networks"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-6 (induced by source paper "billion scale network embedding with iterative random projection"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "struc2vec learning node representations from structural identity"
    - "timers error bounded svd restart on dynamic networks"
    - "a survey on network embedding"
    - "attributed network embedding for learning in a dynamic environment"
    - "structural deep embedding for hyper networks"
    - "node2vec scalable feature learning for networks"
    - "task guided and path augmented heterogeneous network embedding for author identification"

Connected through hyperedge HE-7 (induced by source paper "a tutorial on network embeddings"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "sne signed network embedding"
    - "label noise reduction in entity typing by heterogeneous partial label embedding"
    - "node2vec scalable feature learning for networks"
    - "a general framework for content enhanced network representation learning"
    - "a neural network approach to joint modeling social networks and mobile trajectories"
    - "semi supervised classification with graph convolutional networks"
    - "don t walk skip online learning of multi scale network embeddings"

Connected through hyperedge HE-8 (induced by source paper "principled multilayer network embedding"):
  with members:
    - "spectral clustering methods for multiplex networks"
    - "name disambiguation in anonymized graphs using network embedding"
    - "community detection link prediction and layer interdependence in multilayer networks"
    - "multilayer spectral graph clustering via convex layer aggregation"
    - "higher order organization of complex networks"
    - "node2vec scalable feature learning for networks"
    - "clustering network layers with the strata multilayer stochastic block model"
    - "trust from the past bayesian personalized ranking based link prediction in knowledge 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.SI(Social and Information Networks)