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: "compressive sensing over graphs"
  Abstract: In this paper, motivated by network inference and tomography applications, we study the problem of compressive sensing for sparse signal vectors over graphs. In particular, we are interested in recovering sparse vectors representing the properties of the edges from a graph. Unlike existing compressive sensing results, the collective additive measurements we are allowed to take must follow connected paths over the underlying graph. For a sufficiently connected graph with $n$ nodes, it is shown that, using $O(k \log(n))$ path measurements, we are able to recover any $k$-sparse link vector (with no more than $k$ nonzero elements), even though the measurements have to follow the graph path constraints. We further show that the computationally efficient $\ell_1$ minimization can provide theoretical guarantees for inferring such $k$-sparse vectors with $O(k \log(n))$ path measurements from the graph.

Connected through hyperedge HE-1 (induced by source paper "neural network tomography"):
  with members:
    - "tight bounds for maximal identifiability of failure nodes in boolean network tomography"
    - "neural collaborative filtering"
    - "network capability in localizing node failures via end to end path measurements"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "compressive closeness in networks"):
  with members:
    - "hellrank a hellinger based centrality measure for bipartite social networks"
    - "distributed algorithms for computation of centrality measures in complex networks"
    - "extracting implicit social relation for social recommendation techniques in user rating prediction"
    - "defining and evaluating network communities based on ground truth"
    - "sparse recovery with graph constraints fundamental limits and measurement construction"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-3 (induced by source paper "link delay estimation using sparse recovery for dynamic network tomography"):
  with members:
    - "link delay estimation via expander graphs"

Connected through hyperedge HE-4 (induced by source paper "sparse recovery over graph incidence matrices"):
  with members:
    - "sparse plus low rank autoregressive identification in neuroimaging time series"

Connected through hyperedge HE-5 (induced by source paper "reflective network tomography based on compressed sensing"):
  with members:
    - "link delay estimation via expander graphs"
    - "synchronization free delay tomography based on compressed sensing"

Connected through hyperedge HE-6 (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"
    - "graph constrained group testing"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

Connected through hyperedge HE-7 (induced by source paper "link delay estimation via expander graphs"):
  with members:
    - "sparse recovery with graph constraints fundamental limits and measurement construction"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

Connected through hyperedge HE-8 (induced by source paper "dynamic network delay cartography"):
  with members:
    - "learning with submodular functions a convex optimization perspective"
    - "network inference from co occurrences"

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.IT(Information Theory)