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: "euclidean distance matrices essential theory algorithms and applications"
  Abstract: Euclidean distance matrices (EDMs) are matrices of the squared distances between points. The definition is deceivingly simple; thanks to their many useful properties, they have found applications in psychometrics, crystallography, machine learning, wireless sensor networks, acoustics, and more. Despite the usefulness of EDMs, they seem to be insufficiently known in the signal processing community. Our goal is to rectify this mishap in a concise tutorial. We review the fundamental properties of EDMs, such as rank or (non)definiteness, and show how the various EDM properties can be used to design algorithms for completing and denoising distance data. Along the way, we demonstrate applications to microphone position calibration, ultrasound tomography, room reconstruction from echoes, and phase retrieval. By spelling out the essential algorithms, we hope to fast-track the readers in applying EDMs to their own problems. The code for all of the described algorithms and to generate the figures in the article is available online at http://lcav.epfl.ch/ivan.dokmanic. Finally, we suggest directions for further research.

Connected through hyperedge HE-1 (induced by source paper "learning graph influence from social interactions"):
  with members:
    - "local tomography of large networks under the low observability regime"
    - "consistent tomography under partial observations over adaptive networks"
    - "social learning over weakly connected graphs"
    - "information exchange and learning dynamics over weakly connected adaptive networks"

Connected through hyperedge HE-2 (induced by source paper "don t take it lightly phasing optical random projections with unknown operators"):
  with members:
    - "holographic phase retrieval and reference design"
    - "localization from incomplete euclidean distance matrix performance analysis for the svd mds approach"
    - "holographic phase retrieval and optimal reference design"
    - "practical sketching algorithms for low rank matrix approximation"
    - "random projections through multiple optical scattering approximating kernels at the speed of light"
    - "orthogonal random features"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "phase retrieval an overview of recent developments"

Connected through hyperedge HE-3 (induced by source paper "interplay between topology and social learning over weak graphs"):
  with members:
    - "local tomography of large networks under the low observability regime"
    - "consistent tomography under partial observations over adaptive networks"
    - "social learning over weakly connected graphs"
    - "information exchange and learning dynamics over weakly connected adaptive networks"
    - "interactive sensing and decision making in social networks"

Connected through hyperedge HE-4 (induced by source paper "sample optimal low rank approximation of distance matrices"):
  with members:
    - "sublinear time low rank approximation of distance matrices"
    - "sublinear time low rank approximation of positive semidefinite matrices"
    - "low rank approximation and regression in input sparsity time"
    - "information equals amortized communication"
    - "randomized algorithms for matrices and data"

Connected through hyperedge HE-5 (induced by source paper "range only collaborative localization for ground vehicles"):
  with members:
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "quaternion kinematics for the error state kalman filter"
    - "cooperative localization by dual foot mounted inertial sensors and inter agent ranging"

Connected through hyperedge HE-6 (induced by source paper "shapes from echoes uniqueness from point to plane distance matrices"):
  with members:
    - "a scalable belief propagation algorithm for radio signal based slam"
    - "omnidirectional bats point to plane distances and the price of uniqueness"
    - "look no beacons optimal all in one echoslam"

Connected through hyperedge HE-7 (induced by source paper "sublinear time low rank approximation of distance matrices"):
  with members:
    - "sublinear time low rank approximation of positive semidefinite matrices"
    - "input sparsity time low rank approximation via ridge leverage score sampling"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"
    - "low rank approximation and regression in input sparsity time"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
    - "the distance geometry of music"

Connected through hyperedge HE-8 (induced by source paper "exact reconstruction of euclidean distance geometry problem using low rank matrix completion"):
  with members:
    - "recovering low rank matrices from few coefficients in any basis"
    - "note on sampling without replacing from a finite collection of matrices"
    - "the power of convex relaxation near optimal matrix completion"
    - "a simpler approach to matrix completion"
    - "matrix completion with noise"
    - "exact matrix completion via convex optimization"

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.OH(Other Computer Science)