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: "graph constrained group testing"
  Abstract: Non-adaptive group testing involves grouping arbitrary subsets of $n$ items into different pools. Each pool is then tested and defective items are identified. A fundamental question involves minimizing the number of pools required to identify at most $d$ defective items. Motivated by applications in network tomography, sensor networks and infection propagation, a variation of group testing problems on graphs is formulated. Unlike conventional group testing problems, each group here must conform to the constraints imposed by a graph. For instance, items can be associated with vertices and each pool is any set of nodes that must be path connected. In this paper, a test is associated with a random walk. In this context, conventional group testing corresponds to the special case of a complete graph on $n$ vertices. #R##N#For interesting classes of graphs a rather surprising result is obtained, namely, that the number of tests required to identify $d$ defective items is substantially similar to what is required in conventional group testing problems, where no such constraints on pooling is imposed. Specifically, if T(n) corresponds to the mixing time of the graph $G$, it is shown that with $m=O(d^2T^2(n)\log(n/d))$ non-adaptive tests, one can identify the defective items. Consequently, for the Erdos-Renyi random graph $G(n,p)$, as well as expander graphs with constant spectral gap, it follows that $m=O(d^2\log^3n)$ non-adaptive tests are sufficient to identify $d$ defective items. Next, a specific scenario is considered that arises in network tomography, for which it is shown that $m=O(d^3\log^3n)$ non-adaptive tests are sufficient to identify $d$ defective items. Noisy counterparts of the graph constrained group testing problem are considered, for which parallel results are developed. We also briefly discuss extensions to compressive sensing on graphs.

Connected through hyperedge HE-1 (induced by source paper "a framework for generalized group testing with inhibitors and its potential application in neuroscience"):
  with members:
    - "improved encoding and decoding for non adaptive threshold group testing"
    - "sublinear decoding schemes for non adaptive group testing with inhibitors"
    - "efficient nonrandom construction and decoding of non adaptive group testing"
    - "computational tradeoffs in biological neural networks self stabilizing winner take all networks"
    - "non adaptive group testing with inhibitors"
    - "near optimal stochastic threshold group testing"
    - "improved constructions for non adaptive threshold group testing"
    - "noise resilient group testing limitations and constructions"

Connected through hyperedge HE-2 (induced by source paper "vertex connectivity measures for node failure identification in boolean network tomography"):
  with members:
    - "tight bounds for maximal identifiability of failure nodes in boolean network tomography"

Connected through hyperedge HE-3 (induced by source paper "nearly optimal sparse group testing"):
  with members:
    - "performance of group testing algorithms with near constant tests per item"
    - "novel impossibility results for group testing"
    - "near optimal noisy group testing via separate decoding of items"
    - "individual testing is optimal for nonadaptive group testing in the linear regime"
    - "improved group testing rates with constant column weight designs"
    - "nonadaptive group testing with random set of defectives"
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "group testing schemes from codes and designs"

Connected through hyperedge HE-4 (induced by source paper "anomaly detection in hierarchical data streams under unknown models"):
  with members:
    - "active anomaly detection in heterogeneous processes"
    - "asymptotically optimal anomaly detection via sequential testing"
    - "active sequential hypothesis testing"
    - "sequentiality and adaptivity gains in active hypothesis testing"
    - "controlled sensing for multihypothesis testing"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "boolean compressed sensing and noisy group testing"
    - "hierarchical heavy hitters with the space saving algorithm"

Connected through hyperedge HE-5 (induced by source paper "forward backward greedy algorithms for atomic norm regularization"):
  with members:
    - "convexity in source separation models geometry and algorithms"
    - "sparse overlapping sets lasso for multitask learning and its application to fmri analysis"
    - "convex relaxations of structured matrix factorizations"
    - "solving oscar regularization problems by proximal splitting algorithms"
    - "tensor decompositions for learning latent variable models"
    - "block coordinate frank wolfe optimization for structural svms"
    - "high dimensional sparse inverse covariance estimation using greedy methods"
    - "orthogonal matching pursuit with replacement"

Connected through hyperedge HE-6 (induced by source paper "strong converses for group testing in the finite blocklength regime"):
  with members:
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "group testing with prior statistics"
    - "bitwise map algorithm for group testing based on holographic transformation"
    - "the capacity of non identical adaptive group testing"
    - "group testing algorithms bounds and simulations"
    - "variable length compression allowing errors extended"
    - "variable length compression allowing errors"
    - "the capacity of adaptive group testing"

Connected through hyperedge HE-7 (induced by source paper "quantitative group testing for heavy hitter detection"):
  with members:
    - "probabilistic group testing under sum observations a parallelizable 2 approximation for entropy loss"
    - "group testing algorithms bounds and simulations"
    - "semiquantitative group testing"
    - "near optimal adaptive compressed sensing"
    - "boolean compressed sensing and noisy group testing"
    - "group testing with probabilistic tests theory design and application"
    - "derandomization and group testing"

Connected through hyperedge HE-8 (induced by source paper "frantic a fast reference based algorithm for network tomography via compressive sensing"):
  with members:
    - "sho fa robust compressive sensing with order optimal complexity measurements and bits"
    - "sparse recovery with graph constraints fundamental limits and measurement construction"
    - "invertible bloom lookup tables"
    - "compressive sensing over graphs"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

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)