human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "towards an extremal network theory robust gdof gain of transmitter cooperation over tin"):
  Abstract: Significant progress has been made recently in Generalized Degrees of Freedom (GDoF) characterizations of wireless interference channels (IC) and broadcast channels (BC) under the assumption of finite precision channel state information at the transmitters (CSIT), especially for smaller or highly symmetric network settings. A critical barrier in extending these results to larger and asymmetric networks is the inherent combinatorial complexity of such networks. Motivated by other fields such as extremal combinatorics and extremal graph theory, we explore the possibility of an extremal network theory, i.e., a study of extremal networks within particular regimes of interest. As our test application, we study the GDoF benefits of transmitter cooperation in a $K$ user IC over the simple scheme of power control and treating interference as Gaussian noise (TIN) for three regimes of interest -- a TIN regime where TIN was shown to be GDoF optimal for the $K$ user interference channel, a CTIN regime where the GDoF region achievable by TIN is convex without time-sharing, and an SLS regime where a simple layered superposition (SLS) scheme is shown to be optimal in the $K$ user MISO BC, albeit only for $K\leq 3$. As our first result, we show that under finite precision CSIT, TIN is GDoF optimal for the $K$ user IC throughout the CTIN regime. Furthermore, under finite precision CSIT, appealing to extremal network theory we obtain the following results. In both TIN and CTIN regimes, we show that the extremal GDoF gain from transmitter cooperation over TIN is bounded regardless of the number of users: the gain is exactly a factor of $3/2$ in the TIN regime, and $2-1/K$ in the CTIN regime, for arbitrary number of users $K>1$. However, in the SLS regime, the gain is $\Theta(\log_2(K))$, i.e., it scales logarithmically with the number of users.

Includes members:
  Member "on multi cell uplink downlink duality with treating inter cell interference as noise":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the optimality of treating inter cell interference as noise in uplink cellular networks")
  Member "optimality of simple layered superposition coding in the 3 user miso bc with finite precision csit":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimality of treating inter cell interference as noise under finite precision csit")
  Member "on the optimality of treating inter cell interference as noise in uplink cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimality of treating inter cell interference as noise under finite precision csit")
  Member "opportunistic treating interference as noise":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimality of treating inter cell interference as noise under finite precision csit")
  Member "k user symmetric m times n mimo interference channel under finite precision csit a gdof perspective":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi layer interference alignment and gdof of the k user asymmetric interference channel")
  Member "aligned image sets and the generalized degrees of freedom of symmetric mimo interference channel with partial csit":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimality of simple layered superposition coding in the 3 user miso bc with finite precision csit")
  Member "network coherence time matters aligned image sets and the degrees of freedom of interference networks with finite precision csit and perfect csir":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "optimality of simple layered superposition coding in the 3 user miso bc with finite precision csit")
  Member "elevated multiplexing and signal space partitioning in the 2 user mimo ic with partial csit":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the separability of parallel miso broadcast channels under partial csit a degrees of freedom region perspective")

Question: Please tell me which class the source paper 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)