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 low rank matrices from o n entries"
  Abstract: How many random entries of an n by m, rank r matrix are necessary to reconstruct the matrix within an accuracy d? We address this question in the case of a random matrix with bounded rank, whereby the observed entries are chosen uniformly at random. We prove that, for any d>0, C(r,d)n observations are sufficient. Finally we discuss the question of reconstructing the matrix efficiently, and demonstrate through extensive simulations that this task can be accomplished in nPoly(log n) operations, for small rank.

Connected through hyperedge HE-1 (induced by source paper "a tensor based method for missing traffic data completion"):
  with members:
    - "recovering low rank matrices from few coefficients in any basis"
    - "robust principal component analysis exact recovery of corrupted low rank matrices"
    - "the power of convex relaxation near optimal matrix completion"
    - "uniqueness of low rank matrix completion by rigidity theory"
    - "set an algorithm for consistent matrix completion"
    - "guaranteed rank minimization via singular value projection"
    - "matrix completion from noisy entries"
    - "matrix completion from a few entries"

Connected through hyperedge HE-2 (induced by source paper "a channel coding perspective of collaborative filtering"):
  with members:
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from a few entries"
    - "a channel coding perspective of recommendation systems"
    - "matrix completion with noise"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-3 (induced by source paper "graphical models concepts in compressed sensing"):
  with members:
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "recovering low rank matrices from few coefficients in any basis"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "a simple message passing algorithm for compressed sensing"
    - "a probabilistic and ripless theory of compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "model based compressive sensing"

Connected through hyperedge HE-4 (induced by source paper "message passing inference on a factor graph for collaborative filtering"):
  with members:
    - "probabilistic latent semantic analysis"
    - "set an algorithm for consistent matrix completion"
    - "matrix completion from noisy entries"
    - "a channel coding perspective of recommendation systems"
    - "estimating random variables from random sparse observations"

Connected through hyperedge HE-5 (induced by source paper "information theoretic bounds for low rank matrix completion"):
  with members:
    - "the power of convex relaxation near optimal matrix completion"
    - "uniqueness of low rank matrix completion by rigidity theory"
    - "matrix completion with noise"

Connected through hyperedge HE-6 (induced by source paper "matrix completion from a few entries"):
  with members:
    - "the power of convex relaxation near optimal matrix completion"
    - "uniqueness of low rank matrix completion by rigidity theory"
    - "set an algorithm for consistent matrix completion"
    - "guaranteed rank minimization via singular value projection"
    - "matrix completion from noisy entries"
    - "matrix completion with noise"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-7 (induced by source paper "uniqueness of low rank matrix completion by rigidity theory"):
  with members:
    - "matrix completion from a few entries"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-8 (induced by source paper "a channel coding perspective of recommendation systems"):
  with members:
    - "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.DS(Data Structures and Algorithms)