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: "semidefinite programs on sparse random graphs"
  Abstract: Denote by $A$ the adjacency matrix of an Erd\H{o}s-Renyi graph with bounded average degree. We consider the problem of maximizing $\langle A-{\mathbb E}\{A\},X\rangle$ over the set of positive semidefinite matrices $X$ with diagonal entries $X_{ii}=1$. We prove that for large (bounded) average degree $\gamma$, the value of this semidefinite program (SDP) is -with high probability- $2n\sqrt{\gamma} + n\, o(\sqrt{\gamma})+ o(n)$. #R##N#Our proof is based on two tools from different research areas. First, we develop a new `higher-rank' Grothendieck inequality for symmetric matrices. In the present case, our inequality implies that the value of the above SDP is arbitrarily well approximated by optimizing over rank-$k$ matrices for $k$ large but bounded. Second, we use the interpolation method from spin glass theory to approximate this problem by a second one concerning Wigner random matrices instead of sparse graphs. As an application of our results, we prove new bounds on community detection via SDP that are substantially more accurate than the state of the art.

Connected through hyperedge HE-1 (induced by source paper "learning communities in the presence of errors"):
  with members:
    - "multisection in the stochastic block model using semidefinite programming"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "a semidefinite program for unbalanced multisection in the stochastic block model"
    - "how robust are reconstruction thresholds for community detection"
    - "correlation clustering with noisy partial information"
    - "exact recovery in the stochastic block model"
    - "community detection thresholds and the weak ramanujan property"
    - "improved spectral norm bounds for clustering"

Connected through hyperedge HE-2 (induced by source paper "a semidefinite program for unbalanced multisection in the stochastic block model"):
  with members:
    - "multisection in the stochastic block model using semidefinite programming"
    - "learning communities in the presence of errors"
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "how robust are reconstruction thresholds for community detection"
    - "on semidefinite relaxations for the block model"
    - "exact recovery in the stochastic block model"
    - "community detection thresholds and the weak ramanujan property"

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.DM(Discrete Mathematics)