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: "reduced dimension linear transform coding of correlated signals in networks"
  Abstract: A model called the linear transform network (LTN) is proposed to analyze the compression and estimation of correlated signals transmitted over directed acyclic graphs (DAGs). An LTN is a DAG network with multiple source and receiver nodes. Source nodes transmit subspace projections of random correlated signals by applying reduced-dimension linear transforms. The subspace projections are linearly processed by multiple relays and routed to intended receivers. Each receiver applies a linear estimator to approximate a subset of the sources with minimum mean squared error (MSE) distortion. The model is extended to include noisy networks with power constraints on transmitters. A key task is to compute all local compression matrices and linear estimators in the network to minimize end-to-end distortion. The nonconvex problem is solved iteratively within an optimization framework using constrained quadratic programs (QPs). The proposed algorithm recovers as special cases the regular and distributed Karhunen-Loeve transforms (KLTs). Cut-set lower bounds on the distortion region of multi-source, multi-receiver networks are given for linear coding based on convex relaxations. Cut-set lower bounds are also given for any coding strategy based on information theory. The distortion region and compression-estimation tradeoffs are illustrated for different communication demands (e.g., multiple unicast), and graph structures.

Connected through hyperedge HE-1 (induced by source paper "index coding and network coding via rank minimization"):
  with members:
    - "on a duality between recoverable distributed storage and index coding"
    - "quasi linear network coding"
    - "a matrix completion approach to linear index coding problem"
    - "topological interference management through index coding"
    - "bounding multiple unicasts through index coding and locally repairable codes"
    - "on the capacity region for index coding"
    - "index coding capacity how far can one go with only shannon inequalities"
    - "understanding alternating minimization for matrix completion"

Connected through hyperedge HE-2 (induced by source paper "on zero delay source channel coding"):
  with members:
    - "a deterministic annealing approach to witsenhausen s counterexample"
    - "optimization of zero delay mappings for distributed coding by deterministic annealing"
    - "a deterministic annealing approach to optimization of zero delay source channel codes"
    - "data processing bounds for scalar lossy source codes with side information at the decoder"
    - "on conditions for linearity of optimal estimation"

Connected through hyperedge HE-3 (induced by source paper "multihop backhaul compression for the uplink of cloud radio access networks"):
  with members:
    - "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks"
    - "millimeter wave beamforming for wireless backhaul and access in small cell networks"
    - "uplink multicell processing with limited backhaul via per base station successive interference cancellation"
    - "robust and efficient distributed compression for cloud radio access networks"
    - "cascade multiterminal source coding"
    - "interference mitigation through limited receiver cooperation"
    - "approximate capacity of gaussian relay networks"
    - "successive wyner ziv coding scheme and its application to the quadratic gaussian ceo problem"

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)