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: "efficient downlink channel probing and uplink feedback in fdd massive mimo systems"
  Abstract: Massive Multiple-Input Multiple-Output (massive MIMO) is a variant of multi-user MIMO in which the number of antennas at each Base Station (BS) is very large and typically much larger than the number of users simultaneously served. Massive MIMO can be implemented with Time Division Duplexing (TDD) or Frequency Division Duplexing (FDD) operation. FDD massive MIMO systems are particularly desirable due to their implementation in current wireless networks and their efficiency in situations with symmetric traffic and delay-sensitive applications. However, implementing FDD massive MIMO systems is known to be challenging since it imposes a large feedback overhead in the Uplink (UL) to obtain channel state information for the Downlink (DL). In recent years, a considerable amount of research is dedicated to developing methods to reduce the feedback overhead in such systems. In this paper, we use the sparse spatial scattering properties of the environment to achieve this goal. The idea is to estimate the support of the continuous, frequency-invariant scattering function from UL channel observations and use this estimate to obtain the support of the DL channel vector via appropriate interpolation. We use the resulting support estimate to design an efficient DL probing and UL feedback scheme in which the feedback dimension scales proportionally with the sparsity order of DL channel vectors. Since the sparsity order is much less than the number of BS antennas in almost all practically relevant scenarios, our method incurs much less feedback overhead compared with the currently proposed methods in the literature, such as those based on compressed-sensing. We use numerical simulations to assess the performance of our probing-feedback algorithm and compare it with these methods.

Connected through hyperedge HE-1 (induced by source paper "on optimal scheduling for joint spatial division and multiplexing approach in fdd massive mimo"):
  with members:
    - "graph theory based approach to users grouping and downlink scheduling in fdd massive mimo"
    - "fdd massive mimo efficient downlink probing and uplink feedback via active channel sparsification"
    - "antenna grouping based feedback compression for fdd based massive mimo systems"
    - "content aware user clustering and caching in wireless small cell networks"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback"
    - "energy and spectral efficiency of very large multiuser mimo systems"

Connected through hyperedge HE-2 (induced by source paper "multi band covariance interpolation with applications in massive mimo"):
  with members:
    - "compressive estimation of a stochastic process with unknown autocorrelation function"
    - "low complexity and statistically robust beamformer design for massive mimo systems"
    - "truncated polynomial expansion downlink precoders and uplink detectors for massive mimo"
    - "low complexity massive mimo subspace estimation and tracking from low dimensional projections"
    - "channel vector subspace estimation from low dimensional projections"
    - "linear precoding based on polynomial expansion large scale multi cell mimo systems"
    - "mimo downlink scheduling with non perfect channel state knowledge"

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)