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: "reconstructing point sets from distance distributions"
  Abstract: We study the problem of reconstructing the locations $\boldsymbol u_i$ of a set of points from their unlabeled pairwise distance measurements. Instead of recovering $\boldsymbol u_i$ directly, we represent the point-set by its indicator vector $\boldsymbol x\in\{0,1\}^M$ and search for an $\boldsymbol x$ that reproduces the observed distance distribution. We show that the integer constraint on $\boldsymbol x$ can be further relaxed, and recast the unassigned distance geometry problem into a constrained nonconvex optimization problem. We propose a projected gradient descent algorithm to solve it, and derive conditions under which the proposed method converges to a global optimizer $\boldsymbol x^*$ in both noiseless and noisy cases. In addition, we propose several effective initialization strategies that empirically perform well. Compared to conventional greedy build-up approaches that become inoperable in the face of measurement noise, the proposed distance distribution matching approach jointly reconstructs all the sample points and is robust to noise. We substantiate these claims with a number of numerical experiments.

Connected through hyperedge HE-1 (induced by source paper "solving complex quadratic systems with full rank random matrices"):
  with members:
    - "linear spectral estimators and an application to phase retrieval"
    - "nonconvex matrix factorization from rank one measurements"
    - "coordinate descent algorithms for phase retrieval"
    - "phase transitions of spectral initialization for high dimensional nonconvex estimation"
    - "generalized phase retrieval measurement number matrix recovery and beyond"
    - "stable low rank matrix recovery via null space properties"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "phase retrieval via wirtinger flow theory and algorithms"

Connected through hyperedge HE-2 (induced by source paper "permutations unlabeled beyond sampling unknown"):
  with members:
    - "signal recovery from unlabeled samples"
    - "an algebraic geometric approach to shuffled linear regression"
    - "unlabeled sensing with random linear measurements"

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.DS(Data Structures and Algorithms)