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: "max cut and the smallest eigenvalue"
  Abstract: We describe a new approximation algorithm for Max Cut. Our algorithm runs in $\tilde O(n^2)$ time, where $n$ is the number of vertices, and achieves an approximation ratio of $.531$. On instances in which an optimal solution cuts a $1-\epsilon$ fraction of edges, our algorithm finds a solution that cuts a $1-4\sqrt{\epsilon} + 8\epsilon-o(1)$ fraction of edges. #R##N#Our main result is a variant of spectral partitioning, which can be implemented in nearly linear time. Given a graph in which the Max Cut optimum is a $1-\epsilon$ fraction of edges, our spectral partitioning algorithm finds a set $S$ of vertices and a bipartition $L,R=S-L$ of $S$ such that at least a $1-O(\sqrt \epsilon)$ fraction of the edges incident on $S$ have one endpoint in $L$ and one endpoint in $R$. (This can be seen as an analog of Cheeger's inequality for the smallest eigenvalue of the adjacency matrix of a graph.) Iterating this procedure yields the approximation results stated above. #R##N#A different, more complicated, variant of spectral partitioning leads to an $\tilde O(n^3)$ time algorithm that cuts $1/2 + e^{-\Omega(1/\eps)}$ fraction of edges in graphs in which the optimum is $1/2 + \epsilon$.

Connected through hyperedge HE-1 (induced by source paper "adversarial perturbations of opinion dynamics in networks"):
  with members:
    - "minimizing polarization and disagreement in social networks"
    - "opinion maximization in social networks"
    - "how bad is forming your own opinion"
    - "twice ramanujan sparsifiers"
    - "spectral sparsification of graphs"

Connected through hyperedge HE-2 (induced by source paper "hierarchical representation learning in graph neural networks with node decimation pooling"):
  with members:
    - "mincut pooling in graph neural networks"
    - "understanding attention and generalization in graph neural networks"
    - "graph neural networks with convolutional arma filters"
    - "towards interpretable sparse graph representation learning with laplacian pooling"
    - "clique pooling for graph classification"
    - "graph u nets"
    - "edge contraction pooling for graph neural networks"
    - "a non negative factorization approach to node pooling in graph convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "classification of distributed binary labeling problems"):
  with members:
    - "seeing far vs seeing wide volume complexity of local graph problems"
    - "an automatic speedup theorem for distributed problems"
    - "polylogarithmic time deterministic network decomposition and distributed derandomization"
    - "lower bounds for maximal matchings and maximal independent sets"
    - "how much does randomness help with locally checkable problems"
    - "almost global problems in the local model"
    - "the distributed complexity of locally checkable problems on paths is decidable"
    - "hardness of minimal symmetry breaking in distributed computing"

Connected through hyperedge HE-4 (induced by source paper "an mbo scheme for clustering and semi supervised clustering of signed networks"):
  with members:
    - "node classification for signed networks using diffuse interface methods"
    - "spectral theory of unsigned and signed graphs applications to graph clustering a survey"
    - "a survey of signed network mining in social media"
    - "a method based on total variation for network modularity optimization using the mbo scheme"
    - "notes on elementary spectral graph theory applications to graph clustering using normalized cuts"
    - "eigenvector synchronization graph rigidity and the molecule problem"
    - "extracting spatial information from networks with low order eigenvectors"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-5 (induced by source paper "network density of states"):
  with members:
    - "approximating the spectrum of a graph"
    - "community structure and scale free collections of erdős renyi graphs"

Connected through hyperedge HE-6 (induced by source paper "sherali adams strikes back"):
  with members:
    - "high dimensional estimation via sum of squares proofs"
    - "the power of sum of squares for detecting hidden structures"
    - "strongly refuting random csps below the spectral threshold"
    - "how to refute a random csp"
    - "approximate constraint satisfaction requires large lp relaxations"
    - "subsampling mathematical relaxations and average case complexity"

Connected through hyperedge HE-7 (induced by source paper "hermitian laplacians and a cheeger inequality for the max 2 lin problem"):
  with members:
    - "partitioning well clustered graphs spectral clustering works"
    - "spectral algorithms for unique games"
    - "nearly linear time algorithms for preconditioning and solving symmetric diagonally dominant linear systems"

Connected through hyperedge HE-8 (induced by source paper "graph energies of egocentric networks and their correlation with vertex centrality measures"):
  with members:
    - "the power of convex relaxation near optimal matrix completion"
    - "exact matrix completion via convex optimization"

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)