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: "capacity of gaussian channels with duty cycle and power constraints"
  Abstract: In many wireless communication systems, radios are subject to a duty cycle constraint, that is, a radio only actively transmits signals over a fraction of the time. For example, it is desirable to have a small duty cycle in some low power systems; a half-duplex radio cannot keep transmitting if it wishes to receive useful signals; and a cognitive radio needs to listen and detect primary users frequently. This work studies the capacity of scalar discrete-time Gaussian channels subject to duty cycle constraint as well as average transmit power constraint. An idealized duty cycle constraint is first studied, which can be regarded as a requirement on the minimum fraction of nontransmissions or zero symbols in each codeword. A unique discrete input distribution is shown to achieve the channel capacity. In many situations, numerically optimized on-off signaling can achieve much higher rate than Gaussian signaling over a deterministic transmission schedule. This is in part because the positions of nontransmissions in a codeword can convey information. Furthermore, a more realistic duty cycle constraint is studied, where the extra cost of transitions between transmissions and nontransmissions due to pulse shaping is accounted for. The capacity-achieving input is no longer independent over time and is hard to compute. A lower bound of the achievable rate as a function of the input distribution is shown to be maximized by a first-order Markov input process, the distribution of which is also discrete and can be computed efficiently. The results in this paper suggest that, under various duty cycle constraints, departing from the usual paradigm of intermittent packet transmissions may yield substantial gain.

Connected through hyperedge HE-1 (induced by source paper "on the capacity of the awgn channel with additive radar interference"):
  with members:
    - "on the boundedness of the support of optimal input measures for rayleigh fading channels"

Connected through hyperedge HE-2 (induced by source paper "on the optimality of simple schedules for networks with multiple half duplex relays"):
  with members:
    - "cut set bound is loose for gaussian relay networks"
    - "on the gaussian half duplex relay channel"
    - "gaussian half duplex relay networks improved constant gap and connections with the assignment problem"
    - "achieving the capacity of the n relay gaussian diamond network within log n bits"
    - "on capacity and optimal scheduling for the half duplex multiple relay channel"
    - "efficient capacity computation and power optimization for relay networks"
    - "wireless network information flow a deterministic approach"
    - "learning with submodular functions a convex optimization perspective"

Connected through hyperedge HE-3 (induced by source paper "capacity of the two hop half duplex relay channel"):
  with members:
    - "on the gaussian half duplex relay channel"
    - "bits through deterministic relay cascades with half duplex constraint"
    - "wireless network information flow a deterministic approach"

Connected through hyperedge HE-4 (induced by source paper "minimax filtering via relations between information and estimation"):
  with members:
    - "information theoretic limits on learning stochastic differential equations"
    - "directed information causal estimation and communication in continuous time"
    - "mutual information relative entropy and estimation in the poisson channel"

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)