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: "rank minimization over finite fields fundamental limits and coding theoretic interpretations"
  Abstract: This paper establishes information-theoretic limits for estimating a finite-field low-rank matrix given random linear measurements of it. These linear measurements are obtained by taking inner products of the low-rank matrix with random sensing matrices. Necessary and sufficient conditions on the number of measurements required are provided. It is shown that these conditions are sharp and the minimum-rank decoder is asymptotically optimal. The reliability function of this decoder is also derived by appealing to de Caen's lower bound on the probability of a union. The sufficient condition also holds when the sensing matrices are sparse-a scenario that may be amenable to efficient decoding. More precisely, it is shown that if the n × n-sensing matrices contain, on average, Ω(nlog n) entries, the number of measurements required is the same as that when the sensing matrices are dense and contain entries drawn uniformly at random from the field. Analogies are drawn between the aforementioned results and rank-metric codes in the coding theory literature. In fact, we are also strongly motivated by understanding when minimum rank distance decoding of random rank-metric codes succeeds. To this end, we derive minimum distance properties of equiprobable and sparse rank-metric codes. These distance properties provide a precise geometric interpretation of the fact that the sparse ensemble requires as few measurements as the dense one.

Connected through hyperedge HE-1 (induced by source paper "almost everywhere injectivity conditions for the matrix recovery problem"):
  with members:
    - "on the 2d phase retrieval problem"
    - "generalized phase retrieval measurement number matrix recovery and beyond"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"
    - "phase transitions in phase retrieval"
    - "low rank matrix recovery from rank one measurements"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "phase retrieval via matrix completion"

Connected through hyperedge HE-2 (induced by source paper "subspace aware index codes"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "a sparse and low rank optimization framework for index coding via riemannian optimization"
    - "index coding and network coding via rank minimization"
    - "index coding with coded side information"
    - "caching and coded multicasting multiple groupcast index coding"
    - "quasi linear network coding"
    - "an equivalence between network coding and index coding"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-3 (induced by source paper "almost everywhere matrix recovery"):
  with members:
    - "on the 2d phase retrieval problem"
    - "generalized phase retrieval measurement number matrix recovery and beyond"
    - "phase transitions in phase retrieval"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "phase retrieval via matrix completion"

Connected through hyperedge HE-4 (induced by source paper "index coding with coded side information"):
  with members:
    - "topological interference management through index coding"
    - "bounding multiple unicasts through index coding and locally repairable codes"
    - "local graph coloring and index coding"
    - "fundamental limits of caching"
    - "an equivalence between network coding and index coding"
    - "broadcasting with side information"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-5 (induced by source paper "on the equivalence between low rank matrix completion and tensor rank"):
  with members:
    - "the power of convex relaxation near optimal matrix completion"
    - "symmetric tensor decomposition"
    - "most tensor problems are np hard"
    - "matrix completion from a few entries"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-6 (induced by source paper "computational limits for matrix completion"):
  with members:
    - "algorithms and hardness for robust subspace recovery"
    - "the power of convex relaxation near optimal matrix completion"
    - "a simpler approach to matrix completion"
    - "exact matrix completion via convex optimization"
    - "approximate graph coloring by semidefinite programming"

Connected through hyperedge HE-7 (induced by source paper "on identity testing of tensors low rank recovery and compressed sensing"):
  with members:
    - "recovering low rank matrices from few coefficients in any basis"
    - "subspace expanders and matrix rank minimization"
    - "information theoretic bounds for tensor rank minimization over finite fields"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "on the compressed measurements over finite fields sparse or dense sampling"):
  with members:
    - "group testing with probabilistic tests theory design and application"
    - "distributed sensor failure detection in sensor networks"

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)