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: "nuclear norm minimization for the planted clique and biclique problems"
  Abstract: We consider the problems of finding a maximum clique in a graph and finding a maximum-edge biclique in a bipartite graph. Both problems are NP-hard. We write both problems as matrix-rank minimization and then relax them using the nuclear norm. This technique, which may be regarded as a generalization of compressive sensing, has recently been shown to be an effective way to solve rank optimization problems. In the special cases that the input graph has a planted clique or biclique (i.e., a single large clique or biclique plus diversionary edges), our algorithm successfully provides an exact solution to the original instance. For each problem, we provide two analyses of when our algorithm succeeds. In the first analysis, the diversionary edges are placed by an adversary. In the second, they are placed at random. In the case of random edges for the planted clique problem, we obtain the same bound as Alon, Krivelevich and Sudakov as well as Feige and Krauthgamer, but we use different techniques.

Connected through hyperedge HE-1 (induced by source paper "average case lower bounds for learning sparse mixtures robust estimation and semirandom adversaries"):
  with members:
    - "how hard is robust mean estimation"
    - "optimal average case reductions to sparse pca from weak assumptions to strong hardness"
    - "computational hardness of certifying bounds on constrained pca problems"
    - "efficient algorithms for outlier robust regression"
    - "reducibility and computational lower bounds for problems with planted sparse structure"
    - "robust sparse estimation tasks in high dimensions"
    - "the power of sum of squares for detecting hidden structures"
    - "bayesian estimation from few samples community detection and related problems"

Connected through hyperedge HE-2 (induced by source paper "the average case complexity of counting cliques in erdos renyi hypergraphs"):
  with members:
    - "optimal average case reductions to sparse pca from weak assumptions to strong hardness"
    - "reducibility and computational lower bounds for problems with planted sparse structure"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "on independent sets in random graphs"
    - "incoherence optimal matrix completion"
    - "hidden cliques and the certification of the restricted isometry property"

Connected through hyperedge HE-3 (induced by source paper "clustering degree corrected stochastic block model with outliers"):
  with members:
    - "robust hierarchical clustering"
    - "community detection thresholds and the weak ramanujan property"
    - "clustering partially observed graphs via convex optimization"

Connected through hyperedge HE-4 (induced by source paper "on the complexity of robust pca and l1 norm low rank matrix approximation"):
  with members:
    - "input sparsity and hardness for robust subspace approximation"
    - "recursive robust pca or recursive sparse recovery in large but structured noise"
    - "robust pca via outlier pursuit"
    - "robust principal component analysis"

Connected through hyperedge HE-5 (induced by source paper "how well do local algorithms solve semidefinite programs"):
  with members:
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "improved sum of squares lower bounds for hidden clique and hidden submatrix problems"
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "how robust are reconstruction thresholds for community detection"
    - "sum of squares lower bounds for planted clique"
    - "phase transitions in semisupervised clustering of sparse networks"
    - "exact recovery in the stochastic block model"
    - "multireference alignment using semidefinite programming"

Connected through hyperedge HE-6 (induced by source paper "finding a low rank basis in a matrix subspace"):
  with members:
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "complete dictionary recovery over the sphere"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "rounding sum of squares relaxations"
    - "generalized wong sequences and their applications to edmonds problems"
    - "exact recovery of sparsely used dictionaries"

Connected through hyperedge HE-7 (induced by source paper "improved sum of squares lower bounds for hidden clique and hidden submatrix problems"):
  with members:
    - "sum of squares proofs and the quest toward optimal algorithms"
    - "association schemes non commutative polynomial concentration and sum of squares lower bounds for planted clique"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "statistical algorithms and a lower bound for detecting planted clique"

Connected through hyperedge HE-8 (induced by source paper "a simple spectral algorithm for recovering planted partitions"):
  with members:
    - "breaking the small cluster barrier of graph clustering"

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)