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: "on the effect of randomness on planted 3 coloring models"
  Abstract: We present the hosted coloring framework for studying algorithmic and hardness results for the $k$-coloring problem. There is a class ${\cal H}$ of host graphs. One selects a graph $H\in{\cal H}$ and plants in it a balanced $k$-coloring (by partitioning the vertex set into $k$ roughly equal parts, and removing all edges within each part). The resulting graph $G$ is given as input to a polynomial time algorithm that needs to $k$-color $G$ (any legal $k$-coloring would do -- the algorithm is not required to recover the planted $k$-coloring). Earlier planted models correspond to the case that ${\cal H}$ is the class of all $n$-vertex $d$-regular graphs, a member $H\in{\cal H}$ is chosen at random, and then a balanced $k$-coloring is planted at random. Blum and Spencer [1995] designed algorithms for this model when $d=n^{\delta}$ (for $0<\delta\le1$), and Alon and Kahale [1997] managed to do so even when $d$ is a sufficiently large constant. #R##N#The new aspect in our framework is that it need not involve randomness. In one model within the framework (with $k=3$) $H$ is a $d$ regular spectral expander (meaning that except for the largest eigenvalue of its adjacency matrix, every other eigenvalue has absolute value much smaller than $d$) chosen by an adversary, and the planted 3-coloring is random. We show that the 3-coloring algorithm of Alon and Kahale [1997] can be modified to apply to this case. In another model $H$ is a random $d$-regular graph but the planted balanced $3$-coloring is chosen by an adversary, after seeing $H$. We show that for a certain range of average degrees somewhat below $\sqrt{n}$, finding a 3-coloring is NP-hard. Together these results (and other results that we have) help clarify which aspects of randomness in the planted coloring model are the key to successful 3-coloring algorithms.

Connected through hyperedge HE-1 (induced by source paper "approximation algorithms for partially colorable graphs"):
  with members:
    - "a new algorithm for the robust semi random independent set problem"
    - "does robustness imply tractability a lower bound for planted clique in the semi random model"
    - "combinatorial coloring of 3 colorable graphs"
    - "faster parameterized algorithms using linear programming"
    - "rounding semidefinite programming hierarchies via global correlation"
    - "approximate graph coloring by semidefinite programming"

Connected through hyperedge HE-2 (induced by source paper "towards learning sparsely used dictionaries with arbitrary supports"):
  with members:
    - "outlier robust moment estimation via sum of squares"
    - "clustering semi random mixtures of gaussians"
    - "an improved analysis of the er spud dictionary learning algorithm"
    - "polynomial time tensor decompositions with sum of squares"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "simple efficient and neural algorithms for sparse coding"
    - "a semidefinite program for unbalanced multisection in the stochastic block model"
    - "learning communities in the presence of errors"

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.CC(Computational Complexity)