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: "learning based compressive subsampling"
  Abstract: The problem of recovering a structured signal   $\text{x} \in {\mathbb C}^p$   from a set of dimensionality-reduced linear measurements   $\text{b} = \text{Ax}$   arises in a variety of applications, such as medical imaging, spectroscopy, Fourier optics, and computerized tomography. Due to computational and storage complexity or physical constraints imposed by the problem, the measurement matrix   $\text{A} \in {\mathbb C}^{n \times p}$   is often of the form   $\text{A} = \text{P}_{\Omega}{{\Psi}}$   for some orthonormal basis matrix   ${{\Psi}}\in {\mathbb C}^{p \times p}$   and subsampling operator   $\text{P}_{\Omega}: {\mathbb C}^{p} \rightarrow {\mathbb C}^{n}$   that selects the rows indexed by   $\Omega$  . This raises the fundamental question of how best to choose the index set   $\Omega$   in order to optimize the recovery performance. Previous approaches to addressing this question rely on nonuniform  random  subsampling using application-specific knowledge of the structure of   $\text{x}$  . In this paper, we instead take a principled learning-based approach in which a  fixed  index set is chosen based on a set of training signals   $\text{x}_1,\ldots,\text{x}_m$  . We formulate combinatorial optimization problems seeking to maximize the energy captured in these signals in an average-case or worst-case sense, and we show that these can be efficiently solved either exactly or approximately via the identification of modularity and submodularity structures. We provide both deterministic and statistical theoretical guarantees showing how the resulting measurement matrices perform on signals differing from the training signals, and we provide numerical examples showing our approach to be effective on a variety of data sets.

Connected through hyperedge HE-1 (induced by source paper "learning space partitions for nearest neighbor search"):
  with members:
    - "a model for learned bloom filters and optimizing by sandwiching"
    - "sanns scaling up secure approximate k nearest neighbors search"
    - "approximate nearest neighbor search in high dimensions"
    - "competitive caching with machine learned advice"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "the case for learned index structures"
    - "billion scale similarity search with gpus"
    - "learning combinatorial optimization algorithms over graphs"

Connected through hyperedge HE-2 (induced by source paper "learning based low rank approximations"):
  with members:
    - "a model for learned bloom filters and optimizing by sandwiching"
    - "learning to branch"
    - "hyperspectral image dataset for benchmarking on salient object detection"
    - "competitive caching with machine learned advice"
    - "the case for learned index structures"
    - "learning combinatorial optimization algorithms over graphs"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "lazysvd even faster svd decomposition yet without agonizing pain"

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)