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: "index coded psk modulation"
  Abstract: In this paper we consider noisy index coding problem over AWGN channel. We give an algorithm to map the index coded bits to appropriate sized PSK symbols such that for the given index code, in general, the receiver with large amount of side information will gain in probability of error performance compared to the ones with lesser amount, depending upon the index code used. We call this the \textbf{PSK side information coding gain}. Also, we show that receivers with large amount of side information obtain this coding gain in addition to the bandwidth gain whereas receivers with lesser amount of side information trade off this coding gain with bandwidth gain. Moreover, in general, the difference between the best and worst performance among the receivers is shown to be proportional to the length of the index code employed.

Connected through hyperedge HE-1 (induced by source paper "layered space time index coding"):
  with members:
    - "golden coded index coding"
    - "index codes for the gaussian broadcast channel using quadrature amplitude modulation"
    - "lattice index coding"
    - "optimal coding schemes for the three receiver awgn broadcast channel with receiver message side information"
    - "lattice index codes from algebraic number fields"
    - "joint network and gelfand pinsker coding for 3 receiver gaussian broadcast channels with receiver message side information"
    - "fundamental limits of caching"
    - "broadcast capacity region of two phase bidirectional relaying"

Connected through hyperedge HE-2 (induced by source paper "maximum likelihood decoder for index coded psk modulation for priority ordered receivers"):
  with members:
    - "on the number of optimal index codes"
    - "index codes for the gaussian broadcast channel using quadrature amplitude modulation"
    - "on the number of optimal linear index codes for unicast index coding problems"
    - "optimal index codes for a class of multicast networks with receiver side information"

Connected through hyperedge HE-3 (induced by source paper "noisy index coding with psk and qam"):
  with members:
    - "index codes for the gaussian broadcast channel using quadrature amplitude modulation"
    - "noisy index coding with quadrature amplitude modulation qam"
    - "optimal index codes for a class of multicast networks with receiver side information"
    - "error correction for index coding with side information"

Connected through hyperedge HE-4 (induced by source paper "optimal vector linear index codes for some symmetric side information problems"):
  with members:
    - "noisy index coding with psk and qam"
    - "optimal scalar linear index codes for some symmetric multiple unicast problems"
    - "a lifting construction for scalar linear index codes"
    - "critical graphs in index coding"
    - "index coding an interference alignment perspective"
    - "optimal index codes for a class of multicast networks with receiver side information"

Connected through hyperedge HE-5 (induced by source paper "optimal scalar linear index codes for some symmetric multiple unicast problems"):
  with members:
    - "critical graphs in index coding"
    - "index coding an interference alignment perspective"
    - "optimal index codes for a class of multicast networks with receiver side information"
    - "error correction for index coding with side information"
    - "index coding via linear programming"

Connected through hyperedge HE-6 (induced by source paper "a lifting construction for scalar linear index codes"):
  with members:
    - "optimal scalar linear index codes for some symmetric multiple unicast problems"
    - "critical graphs in index coding"
    - "index coding an interference alignment perspective"
    - "optimal index codes for a class of multicast networks with receiver side information"

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)