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: "a general purpose algorithm for counting simple cycles and simple paths of any length"
  Abstract: We describe a general purpose algorithm for counting simple cycles and simple paths of any length $\ell$ on a (weighted di)graph on $N$ vertices and $M$ edges, achieving a time complexity of $O\left(N+M+\big(\ell^\omega+\ell\Delta\big) |S_\ell|\right)$. In this expression, $|S_\ell|$ is the number of (weakly) connected induced subgraphs of $G$ on at most $\ell$ vertices, $\Delta$ is the maximum degree of any vertex and $\omega$ is the exponent of matrix multiplication. We compare the algorithm complexity both theoretically and experimentally with most of the existing algorithms for the same task. These comparisons show that the algorithm described here is the best general purpose algorithm for the class of graphs where $(\ell^{\omega-1}\Delta^{-1}+1) |S_\ell|\leq |\text{Cycle}_\ell|$, with $|\text{Cycle}_\ell|$ the total number of simple cycles of length at most $\ell$, including backtracks and self-loops. On Erdős-Renyi random graphs, we find empirically that this happens when the edge probability is larger than circa $4/N$. In addition, we show that some real-world networks also belong to this class. Finally, the algorithm permits the enumeration of simple cycles and simple paths on networks where vertices are labeled from an alphabet on $n$ letters with a time complexity of $O\left(N+M+\big(n^\ell\ell^\omega+\ell\Delta\big) |S_\ell|\right)$. A Matlab implementation of the algorithm proposed here is available for download.

Connected through hyperedge HE-1 (induced by source paper "signed network structural analysis and applications with a focus on balance theory"):
  with members:
    - "balance and frustration in signed networks"
    - "measuring partial balance in signed networks"
    - "computing the line index of balance using integer programming optimisation"
    - "evaluating balance on social networks from their simple cycles"
    - "spectral theory of unsigned and signed graphs applications to graph clustering a survey"
    - "an exact method for computing the frustration index in signed networks using binary programming"
    - "powers of tensors and fast matrix multiplication"
    - "applications of structural balance in signed social networks"

Connected through hyperedge HE-2 (induced by source paper "balance and frustration in signed networks"):
  with members:
    - "measuring partial balance in signed networks"
    - "computing the line index of balance using integer programming optimisation"
    - "evaluating balance on social networks from their simple cycles"
    - "an exact method for computing the frustration index in signed networks using binary programming"
    - "applications of structural balance in signed social networks"
    - "correlation clustering with a fixed number of clusters"

Connected through hyperedge HE-3 (induced by source paper "measuring partial balance in signed networks"):
  with members:
    - "evaluating balance on social networks from their simple cycles"
    - "spectral theory of unsigned and signed graphs applications to graph clustering a survey"
    - "an exact method for computing the frustration index in signed networks using binary programming"
    - "applications of structural balance in signed social networks"

Connected through hyperedge HE-4 (induced by source paper "evaluating balance on social networks from their simple cycles"):
  with members:
    - "measuring partial balance in signed networks"
    - "an exact method for computing the frustration index in signed networks using binary programming"
    - "a modelling and computational study of the frustration index in signed networks"
    - "a polynomial delay algorithm for generating connected induced subgraphs of a given cardinality"
    - "applications of structural balance in signed 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.DS(Data Structures and Algorithms)