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: "submodular and supermodular multi labeling and vertex happiness"
  Abstract: In this paper, we investigate the submodular multi-labeling (Sub-ML) problem, a more general version of the submodular multiway partition (Sub-MP), which captures many cut problems as special cases, including the edge-/node-weighted multiway cut and the hypergraph multiway cut. We also study the complement of Sub-ML, the supermodular multi-labeling (Sup-ML) problem. We propose a convex (concave, respectively) relaxation for the Sub-ML (Sup-ML, respectively) problem based on the Lov\'{a}sz extension, which can be solved in polynomial time. By applying a randomized rounding, we prove that Sub-ML can be approximated within a factor of $2 - \frac{2}{k}$ and Sup-ML can be approximated within a factor of $\frac{2}{k}$, where $k$ is the number of labels. In addition, we find that a recently studied vertex-coloring problem, the maximum happy vertices (MHV) problem, can be casted as a special case of Sup-ML. Hence, MHV can be approximated within $\frac{2}{k}$, which improves the previous best $\frac{1}{k}$-approximation. Based on the associated LP relaxation, we further prove that the $\frac{2}{k}$-approximation is the best possible for MHV. For the complementary minimum unhappy vertices (MUHV) problem, casted as a special case of Sub-ML, it can be approximated within $2 - \frac{2}{k}$ too; we prove that $2 - \frac{2}{k}$ is the best possible based on the associated LP relaxation; lastly, we show that a $(2 - \frac{2}{k} - \epsilon)$-approximation for MUHV is NP-hard under the Unique Games Conjecture, for any positive $\epsilon$.

Connected through hyperedge HE-1 (induced by source paper "optimizing item and subgroup configurations for social aware vr shopping"):
  with members:
    - "solving linear programs in the current matrix multiplication time"
    - "neural collaborative filtering"
    - "spmc socially aware personalized markov chains for sparse sequential recommendation"
    - "a multilayer network dataset of interaction and influence spreading in a virtual world"
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "from group recommendations to group formation"
    - "when social influence meets item inference"

Connected through hyperedge HE-2 (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"
    - "algorithms and hardness results for happy coloring problems"

Connected through hyperedge HE-3 (induced by source paper "lower bounds for the happy coloring problems"):
  with members:
    - "the parameterized complexity of happy colorings"
    - "algorithms and hardness results for happy coloring problems"
    - "on problems as hard as cnf sat"
    - "exponential time complexity of the permanent and the tutte polynomial"
    - "kernelization of packing problems"
    - "quasirandom rumor spreading"

Connected through hyperedge HE-4 (induced by source paper "on happy colorings cuts and structural parameterizations"):
  with members:
    - "the parameterized complexity of happy colorings"
    - "algorithms and hardness results for happy coloring problems"
    - "dominating set is fixed parameter tractable in claw free graphs"

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)