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: "a channel coding perspective of collaborative filtering"
  Abstract: We consider the problem of collaborative filtering from a channel coding perspective. We model the underlying rating matrix as a finite alphabet matrix with block constant structure. The observations are obtained from this underlying matrix through a discrete memoryless channel with a noisy part representing noisy user behavior and an erasure part representing missing data. Moreover, the clusters over which the underlying matrix is constant are unknown. We establish a threshold result for this model: if the largest cluster size is smaller than C1 log(mn) (where the rating matrix is of size m × n), then the underlying matrix cannot be recovered with any estimator, but if the smallest cluster size is larger than C2 log(mn), then we show a polynomial time estimator with asymptotically vanishing probability of error. In the case of uniform cluster size, not only the order of the threshold, but also the constant is identified.

Connected through hyperedge HE-1 (induced by source paper "analysis of a collaborative filter based on popularity amongst neighbors"):
  with members:
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from a few entries"
    - "a channel coding perspective of recommendation systems"
    - "exact matrix completion via convex optimization"
    - "low rank matrix factorization with attributes"

Connected through hyperedge HE-2 (induced by source paper "local popularity based collaborative filters"):
  with members:
    - "matrix completion from a few entries"
    - "a channel coding perspective of recommendation systems"
    - "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.IT(Information Theory)