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: "information theoretic bounds for tensor rank minimization over finite fields"
  Abstract: We consider the problem of noiseless and noisy low-rank tensor completion from a set of random linear measurements. In our derivations, we assume that the entries of the tensor belong to a finite field of arbitrary size and that reconstruction is based on a rank minimization framework. The derived results show that the smallest number of measurements needed for exact reconstruction is upper bounded by the product of the rank, the order and the dimension of a cubic tensor. Furthermore, this condition is also sufficient for unique minimization. Similar bounds hold for the noisy rank minimization scenario, except for a scaling function that depends on the channel error probability.

Connected through hyperedge HE-1 (induced by source paper "rank minimization over finite fields fundamental limits and coding theoretic interpretations"):
  with members:
    - "capacity achieving linear codes with random binary sparse generating matrices"
    - "capacity achieving random sparse linear codes"
    - "information theoretic bounds for low rank matrix completion"
    - "distributed storage codes meet multiple access wiretap channels"
    - "communication over finite field matrix channels"
    - "the power of convex relaxation near optimal matrix completion"
    - "lp decoding meets lp decoding a connection between channel coding and compressed sensing"
    - "guaranteed rank minimization via singular value projection"

Connected through hyperedge HE-2 (induced by source paper "on identity testing of tensors low rank recovery and compressed sensing"):
  with members:
    - "rank minimization over finite fields fundamental limits and coding theoretic interpretations"
    - "recovering low rank matrices from few coefficients in any basis"
    - "subspace expanders and matrix rank minimization"
    - "on the power of adaptivity in sparse recovery"
    - "tensor rank some lower and upper bounds"
    - "blackbox identity testing for bounded top fanin depth 3 circuits the field doesn t matter"
    - "the power of convex relaxation near optimal matrix completion"

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)