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: "algorithms and hardness results for happy coloring problems"
  Abstract: In a vertex-colored graph, an edge is happy if its endpoints have the same color. Similarly, a vertex is happy if all its incident edges are happy. Motivated by the computation of homophily in social networks, we consider the algorithmic aspects of the following Maximum Happy Edges (k-MHE) problem: given a partially k-colored graph G, find an extended full k-coloring of G maximizing the number of happy edges. When we want to maximize the number of happy vertices, the problem is known as Maximum Happy Vertices (k-MHV). We further study the complexity of the problems and their weighted variants. For instance, we prove that for every k >= 3, both problems are NP-complete for bipartite graphs and k-MHV remains hard for split graphs. In terms of exact algorithms, we show both problems can be solved in time O*(2^n), and give an even faster O*(1.89^n)-time algorithm when k = 3. From a parameterized perspective, we give a linear vertex kernel for Weighted k-MHE, where edges are weighted and the goal is to obtain happy edges of at least a specified total weight. Finally, we prove both problems are solvable in polynomial-time when the graph has bounded treewidth or bounded neighborhood diversity.

Connected through hyperedge HE-1 (induced by source paper "maximizing happiness in graphs of bounded clique width"):
  with members:
    - "lower bounds for the happy coloring problems"
    - "on happy colorings cuts and structural parameterizations"
    - "the parameterized complexity of happy colorings"
    - "target set selection parameterized by clique width and maximum threshold"
    - "submodular and supermodular multi labeling and vertex happiness"

Connected through hyperedge HE-2 (induced by source paper "lower bounds for the happy coloring problems"):
  with members:
    - "the parameterized complexity of happy colorings"
    - "on problems as hard as cnf sat"
    - "submodular and supermodular multi labeling and vertex happiness"
    - "exponential time complexity of the permanent and the tutte polynomial"
    - "kernelization of packing problems"
    - "quasirandom rumor spreading"

Connected through hyperedge HE-3 (induced by source paper "on happy colorings cuts and structural parameterizations"):
  with members:
    - "the parameterized complexity of happy colorings"
    - "submodular and supermodular multi labeling and vertex happiness"
    - "dominating set is fixed parameter tractable in claw free graphs"

Connected through hyperedge HE-4 (induced by source paper "simplified algorithmic metatheorems beyond mso treewidth and neighborhood diversity"):
  with members:
    - "parameterized complexity of fair deletion problems"
    - "the graph motif problem parameterized by the structure of the input graph"
    - "on the parameterized complexity of recognizing well covered r l graphs"
    - "anti path cover on sparse graph classes"
    - "target set selection in dense graph classes"
    - "extension complexity mso logic and treewidth"
    - "on the parameterized complexity of computing balanced partitions in graphs"
    - "fixed parameter complexity of distance constrained labeling and uniform channel assignment problems"

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)