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: "fine grained complexity and conditional hardness for sparse graphs"
  Abstract: We consider the fine-grained complexity of sparse graph problems that currently have $\tilde{O}(mn)$ time algorithms, where m is the number of edges and n is the number of vertices in the input graph. This class includes several important path problems on both directed and undirected graphs, including APSP, MWC (minimum weight cycle), and Eccentricities, which is the problem of computing, for each vertex in the graph, the length of a longest shortest path starting at that vertex. #R##N#We introduce the notion of a sparse reduction which preserves the sparsity of graphs, and we present near linear-time sparse reductions between various pairs of graph problems in the $\tilde{O}(mn)$ class. Surprisingly, very few of the known nontrivial reductions between problems in the $\tilde{O}(mn)$ class are sparse reductions. In the directed case, our results give a partial order on a large collection of problems in the $\tilde{O}(mn)$ class (along with some equivalences). In the undirected case we give two nontrivial sparse reductions: from MWC to APSP, and from unweighted ANSC (all nodes shortest cycles) to APSP. The latter reduction also gives an improved algorithm for ANSC (for dense graphs). #R##N#We propose the MWC Conjecture, a new conditional hardness conjecture that the weight of a minimum weight cycle in a directed graph cannot be computed in time polynomially smaller than mn. Our sparse reductions for directed path problems in the $\tilde{O}(mn)$ class establish that several problems in this class, including 2-SiSP (second simple shortest path), Radius, and Eccentricities, are MWCC hard. We also identify Eccentricities as a key problem in the $\tilde{O}(mn)$ class which is simultaneously MWCC-hard, SETH-hard and k-DSH-hard, where SETH is the Strong Exponential Time Hypothesis, and k-DSH is the hypothesis that a dominating set of size k cannot be computed in time polynomially smaller than n^k.

Connected through hyperedge HE-1 (induced by source paper "tight hardness for shortest cycles and paths in sparse graphs"):
  with members:
    - "tree edit distance cannot be computed in strongly subcubic time unless apsp can"
    - "finding even cycles faster via capped k walks"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "the input output complexity of triangle enumeration"
    - "powers of tensors and fast matrix multiplication"
    - "faster all pairs shortest paths via circuit complexity"
    - "algorithmic applications of baur strassen s theorem shortest cycles diameter and matchings"
    - "all pairs shortest paths using bridging sets and rectangular matrix multiplication"

Connected through hyperedge HE-2 (induced by source paper "improved distance queries and cycle counting by frobenius normal form"):
  with members:
    - "powers of tensors and fast matrix multiplication"
    - "faster all pairs shortest paths via circuit complexity"
    - "algorithmic applications of baur strassen s theorem shortest cycles diameter and matchings"
    - "minimum weight cycles and triangles equivalences and algorithms"
    - "all pairs shortest paths using bridging sets and rectangular matrix multiplication"

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)