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: "relaxation based coarsening and multiscale graph organization"
  Abstract: In this paper we generalize and improve the multiscale organization of graphs by introducing a new measure that quantifies the "closeness" between two nodes. The calculation of the measure is linear in the number of edges in the graph and involves just a small number of relaxation sweeps. A similar notion of distance is then calculated and used at each coarser level. We demonstrate the use of this measure in multiscale methods for several important combinatorial optimization problems and discuss the multiscale graph organization.

Connected through hyperedge HE-1 (induced by source paper "recent advances in scalable network generation"):
  with members:
    - "efficiently generating geometric inhomogeneous and hyperbolic random graphs"
    - "exploring randomly wired neural networks for image recognition"
    - "parallel weighted random sampling"
    - "linear work generation of r mat graphs"
    - "egbter capturing degree distribution clustering coefficients and community structure in a single random graph model"
    - "multiscale planar graph generation"
    - "parallel and i o efficient randomisation of massive networks using global curveball trades"
    - "efficient parallel random sampling vectorized cache efficient and online"

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"
    - "netlsd hearing the shape of a graph"
    - "approximate nearest neighbor search in high dimensions"

Connected through hyperedge HE-3 (induced by source paper "elruna elimination rule based network alignment"):
  with members:
    - "regal representation learning based graph alignment"
    - "an iterative global structure assisted labeled network aligner"
    - "spectral alignment of graphs"
    - "advanced coarsening schemes for graph partitioning"

Connected through hyperedge HE-4 (induced by source paper "unsupervised learning of graph hierarchical abstractions with differentiable coarsening and optimal transport"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "graph u nets"
    - "gromov wasserstein learning for graph matching and node embedding"
    - "self attention graph pooling"
    - "solving graph compression via optimal transport"
    - "provably powerful graph networks"
    - "online planner selection with graph neural networks and adaptive scheduling"
    - "graph convolutional neural networks for web scale recommender systems"

Connected through hyperedge HE-5 (induced by source paper "hypergraph partitioning with embeddings"):
  with members:
    - "pytorch biggraph a large scale graph embedding system"
    - "aggregative coarsening for multilevel hypergraph partitioning"
    - "verse versatile graph embeddings from similarity measures"
    - "network flow based refinement for multilevel hypergraph partitioning"
    - "memetic multilevel hypergraph partitioning"
    - "relaxation based coarsening for multilevel hypergraph partitioning"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "solving graph compression via optimal transport"):
  with members:
    - "a unifying framework for spectrum preserving graph sparsification and coarsening"
    - "learning small predictors"
    - "how powerful are graph neural networks"
    - "spectrally approximating large graphs with smaller graphs"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "continuous flow graph transportation distances"
    - "learning with a wasserstein loss"

Connected through hyperedge HE-7 (induced by source paper "fobe and hobe first and high order bipartite embeddings"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "janossy pooling learning deep permutation invariant functions for variable size inputs"
    - "relaxation based coarsening for multilevel hypergraph partitioning"
    - "node2vec scalable feature learning for networks"
    - "single and multi level network sparsification by algebraic distance"
    - "tensorflow a system for large scale machine learning"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-8 (induced by source paper "graph reduction with spectral and cut guarantees"):
  with members:
    - "analyzing inverse problems with invertible neural networks"
    - "spectrally approximating large graphs with smaller graphs"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "geometric deep learning going beyond euclidean data"
    - "a 2 ϵ approximation for maximum weight matching in the semi streaming model"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "compressive spectral clustering"
    - "a multiscale pyramid transform for graph signals"

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.DS(Data Structures and Algorithms)