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: "mimo broadcast channels with block diagonalization and finite rate feedback"
  Abstract: Block diagonalization is a linear precoding technique for the multiple antenna broadcast (downlink) channel that involves transmission of multiple data streams to each receiver such that no multi-user interference is experienced at any of the receivers. This low-complexity scheme operates only a few dB away from capacity but does require very accurate channel knowledge at the transmitter, which can be very difficult to obtain in fading scenarios. We consider a limited feedback system where each receiver knows its channel perfectly, but the transmitter is only provided with a finite number of channel feedback bits from each receiver. Using a random vector quantization argument, we quantify the throughput loss due to imperfect channel knowledge as a function of the feedback level. The quality of channel knowledge must improve proportional to the SNR in order to prevent interference-limitations, and we show that scaling the number of feedback bits linearly with the system SNR is sufficient to maintain a bounded rate loss. Finally, we investigate a simple scalar quantization scheme that is seen to achieve the same scaling behavior as vector quantization.

Connected through hyperedge HE-1 (induced by source paper "tranceiver design using linear precoding in a multiuser mimo system with limited feedback"):
  with members:
    - "minimizing sum mse implies identical downlink and dual uplink power allocations"
    - "limited feedback based block diagonalization for the mimo broadcast channel"
    - "antenna combining for the mimo downlink channel"
    - "mimo broadcast channels with finite rate feedback"
    - "a feedback reduction technique for mimo broadcast channels"

Connected through hyperedge HE-2 (induced by source paper "on the degrees of freedom of finite state compound wireless networks settling a conjecture by weingarten et al"):
  with members:
    - "the approximate capacity of the many to one and one to many gaussian interference channels"
    - "real interference alignment with real numbers"
    - "on the degrees of freedom of the k user gaussian interference channel"
    - "on degrees of freedom region of mimo networks without csit"
    - "interference alignment with asymmetric complex signaling settling the host madsen nosratinia conjecture"
    - "degrees of freedom of the k user m times n mimo interference channel"
    - "interference alignment for line of sight channels"
    - "mimo broadcast channels with finite rate feedback"

Connected through hyperedge HE-3 (induced by source paper "limited feedback based block diagonalization for the mimo broadcast channel"):
  with members:
    - "antenna combining for the mimo downlink channel"
    - "mimo broadcast channels with finite rate feedback"
    - "quantization bounds on grassmann manifolds and applications to mimo communications"
    - "a feedback reduction technique for mimo broadcast channels"

Connected through hyperedge HE-4 (induced by source paper "antenna combining for the mimo downlink channel"):
  with members:
    - "limited feedback based block diagonalization for the mimo broadcast channel"
    - "quantized vs analog feedback for the mimo downlink a comparison between zero forcing based achievable rates"
    - "mimo broadcast channels with finite rate feedback"
    - "a feedback reduction technique for mimo broadcast channels"

Connected through hyperedge HE-5 (induced by source paper "networked mimo with clustered linear precoding"):
  with members:
    - "space division multiple access with a sum feedback rate constraint"
    - "mimo broadcast channels with finite rate feedback"

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)