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: "triangular alignment tame a tensor based approach for higher order network alignment"
  Abstract: Network alignment has extensive applications in comparative interactomics. Traditional approaches aim to simultaneously maximize the number of conserved edges and the underlying similarity of aligned entities. We propose a novel formulation of the network alignment problem that extends topological similarity to higher-order structures and provides a new objective function that maximizes the number of aligned substructures. This objective function corresponds to an integer programming problem, which is NP-hard. Consequently, we identify a closely related surrogate function whose maximization results in a tensor eigenvector problem. Based on this formulation, we present an algorithm called Triangular AlignMEnt (TAME), which attempts to maximize the number of aligned triangles across networks. Using a case study on the NAPAbench dataset, we show that triangular alignment is capable of producing mappings with high node correctness. We further evaluate our method by aligning yeast and human interactomes. Our results indicate that TAME outperforms the state-of-art alignment methods in terms of conserved triangles. In addition, we show that the number of conserved triangles is more significantly correlated, compared to the conserved edge, with node correctness and co-expression of edges. Our formulation and resulting algorithms can be easily extended to arbitrary motifs.

Connected through hyperedge HE-1 (induced by source paper "gromov wasserstein learning for graph matching and node embedding"):
  with members:
    - "learning generative models across incomparable spaces"
    - "gromov wasserstein alignment of word embedding spaces"
    - "graph convolutional neural networks for web scale recommender systems"
    - "the gromov wasserstein distance between networks and stable network invariants"
    - "adversarial text generation via feature mover s distance"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "anonymous walk embeddings"

Connected through hyperedge HE-2 (induced by source paper "kergm kernelized graph matching"):
  with members:
    - "scalable global alignment graph kernel using random features from node embedding to graph embedding"
    - "revisiting random binning features fast convergence and strong parallelizability"
    - "stable and informative spectral signatures for graph matching"
    - "subgraph matching kernels for attributed graphs"
    - "a path following algorithm for the graph matching problem"

Connected through hyperedge HE-3 (induced by source paper "rigid graph alignment"):
  with members:
    - "low rank methods for multiple network alignment"
    - "rdf graph alignment with bisimulation"
    - "spectral alignment of networks"

Connected through hyperedge HE-4 (induced by source paper "spectral alignment of networks"):
  with members:
    - "maximum quadratic assignment problem reduction from maximum label cover and lp based approximation algorithm"
    - "lagrangian relaxation applied to sparse global network alignment"
    - "de anonymizing social networks"

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.CE(Computational Engineering, Finance, and Science)