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: "tight bounds for maximal identifiability of failure nodes in boolean network tomography"
  Abstract: We study maximal identifiability, a measure recently introduced in Boolean Network Tomography to characterize networks' capability to localize failure nodes in end-to-end path measurements. Under standard assumptions on topologies and on monitors placement, we prove tight upper and lower bounds on the maximal identifiability of failure nodes for specific classes of network topologies, such as trees, bounded-degree graphs, $d$-dimensional grids, in both directed and undirected cases. Among other results we prove that directed $d$-dimensional grids with support $n$ have maximal identifiability $d$ using $nd$ monitors; and in the undirected case we show that $2d$ monitors suffice to get identifiability of $d-1$. We then study identifiability under embeddings: we establish relations between maximal identifiability, embeddability and dimension when network topologies are modelled as DAGs. Through our analysis we also refine and generalize results on limits of maximal identifiability recently obtained in [11] and [1]. Our results suggest the design of networks over $N$ nodes with maximal identifiability $\Omega(\sqrt{\log N})$ using $2\sqrt{\log N}$ monitors and heuristics to place monitors and edges in a network to boost maximal identifiability.

Connected through hyperedge HE-1 (induced by source paper "neural network tomography"):
  with members:
    - "neural collaborative filtering"
    - "network capability in localizing node failures via end to end path measurements"
    - "adam a method for stochastic optimization"
    - "compressive sensing over graphs"

Connected through hyperedge HE-2 (induced by source paper "on fundamental bounds of failure identifiability by boolean network tomography"):
  with members:
    - "network capability in localizing node failures via end to end path measurements"
    - "boolean compressed sensing and noisy group testing"

Connected through hyperedge HE-3 (induced by source paper "vertex connectivity measures for node failure identification in boolean network tomography"):
  with members:
    - "graph constrained group testing"

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)