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: "the projected power method an efficient algorithm for joint alignment from pairwise differences"
  Abstract: Various applications involve assigning discrete label values to a collection of objects based on some pairwise noisy data. Due to the discrete---and hence nonconvex---structure of the problem, computing the optimal assignment (e.g.~maximum likelihood assignment) becomes intractable at first sight. This paper makes progress towards efficient computation by focusing on a concrete joint alignment problem---that is, the problem of recovering $n$ discrete variables $x_i \in \{1,\cdots, m\}$, $1\leq i\leq n$ given noisy observations of their modulo differences $\{x_i - x_j~\mathsf{mod}~m\}$. We propose a low-complexity and model-free procedure, which operates in a lifted space by representing distinct label values in orthogonal directions, and which attempts to optimize quadratic functions over hypercubes. Starting with a first guess computed via a spectral method, the algorithm successively refines the iterates via projected power iterations. We prove that for a broad class of statistical models, the proposed projected power method makes no error---and hence converges to the maximum likelihood estimate---in a suitable regime. Numerical experiments have been carried out on both synthetic and real data to demonstrate the practicality of our algorithm. We expect this algorithmic framework to be effective for a broad range of discrete assignment problems.

Connected through hyperedge HE-1 (induced by source paper "optimal learning of joint alignments with a faulty oracle"):
  with members:
    - "joint alignment from pairwise differences with a noisy oracle"
    - "predicting positive and negative links with noisy queries theory practice"
    - "near optimal joint object matching via convex relaxation"
    - "streaming graph partitioning in the planted partition model"
    - "exact and stable recovery of rotations for robust synchronization"

Connected through hyperedge HE-2 (induced by source paper "message passing algorithms for synchronization problems over compact groups"):
  with members:
    - "finite sample analysis of approximate message passing algorithms"
    - "mutual information in rank one matrix estimation"
    - "mutual information for symmetric rank one matrix estimation a proof of the replica formula"
    - "phase transitions in sparse pca"
    - "non unique games over compact groups and orientation estimation in cryo em"
    - "mmse of probabilistic low rank matrix estimation universality with respect to the output channel"
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "information theoretically optimal sparse pca"

Connected through hyperedge HE-3 (induced by source paper "path invariant map networks"):
  with members:
    - "taskonomy disentangling task transfer learning"
    - "robust rotation synchronization via low rank and sparse matrix decomposition"
    - "scannet richly annotated 3d reconstructions of indoor scenes"
    - "data distillation towards omni supervised learning"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "dualgan unsupervised dual learning for image to image translation"
    - "semantic scene completion from a single depth image"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-4 (induced by source paper "bispectrum inversion with application to multireference alignment"):
  with members:
    - "message passing algorithms for synchronization problems over compact groups"
    - "on signal reconstruction from frog measurements"
    - "fourier phase retrieval uniqueness and algorithms"
    - "on the uniqueness of frog methods"
    - "the sample complexity of multi reference alignment"
    - "non convex phase retrieval from stft measurements"
    - "fundamental limits in multi image alignment"
    - "near optimal joint object matching via convex relaxation"

Connected through hyperedge HE-5 (induced by source paper "joint alignment from pairwise differences with a noisy oracle"):
  with members:
    - "optimal learning of joint alignments with a faulty oracle"
    - "predicting positive and negative links with noisy queries theory practice"
    - "clustering via crowdsourcing"
    - "a semidefinite program for unbalanced multisection in the stochastic block model"
    - "beyond triangles a distributed framework for estimating 3 profiles of large graphs"
    - "mathematical and algorithmic analysis of network and biological data"
    - "exact recovery in the stochastic block model"
    - "a tail bound for read k families of functions"

Connected through hyperedge HE-6 (induced by source paper "higher order projected power iterations for scalable multi matching"):
  with members:
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "ds a flexible scalable and provably tight relaxation for matching problems"
    - "consistent multiple graph matching with multi layer random walks synchronization"
    - "multi image semantic matching by mining consistent features"
    - "multi graph matching via affinity optimization with graduated consistency regularization"
    - "a flexible tensor block coordinate ascent scheme for hypergraph matching"
    - "multi image matching via fast alternating minimization"
    - "near optimal joint object matching via convex relaxation"

Connected through hyperedge HE-7 (induced by source paper "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "on the global geometry of sphere constrained sparse blind deconvolution"
    - "fast robust and non convex subspace recovery"
    - "nonconvex matrix factorization from rank one measurements"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "phase retrieval via linear programming fundamental limits and algorithmic improvements"
    - "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing"
    - "recovery guarantees for one hidden layer neural networks"

Connected through hyperedge HE-8 (induced by source paper "global guarantees for enforcing deep generative priors by empirical risk"):
  with members:
    - "how generative adversarial networks and their variants work an overview"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "phase retrieval under a generative prior"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "a convergence theory for deep learning via over parameterization"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"

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)