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 recommendation systems"
  Abstract: Motivated by recommendation systems, we consider the problem of estimating block constant binary matrices (of size $m \times n$) from sparse and noisy observations. The observations are obtained from the underlying block constant matrix after unknown row and column permutations, erasures, and errors. We derive upper and lower bounds on the achievable probability of error. For fixed erasure and error probability, we show that there exists a constant $C_1$ such that if the cluster sizes are less than $C_1 \ln(mn)$, then for any algorithm the probability of error approaches one as $m, n \tends \infty$. On the other hand, we show that a simple polynomial time algorithm gives probability of error diminishing to zero provided the cluster sizes are greater than $C_2 \ln(mn)$ for a suitable constant $C_2$.

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

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"
    - "matrix completion with noise"
    - "learning low rank matrices from o n entries"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-3 (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"
    - "learning low rank matrices from o n entries"
    - "estimating random variables from random sparse observations"

Connected through hyperedge HE-4 (induced by source paper "local popularity based collaborative filters"):
  with members:
    - "a channel coding perspective of collaborative filtering"
    - "matrix completion from a few entries"
    - "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)