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: "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.

Connected through hyperedge HE-1 (induced by source paper "optimality of treating inter cell interference as noise under finite precision csit"):
  with members:
    - "on the optimality of treating inter cell interference as noise downlink cellular networks and uplink downlink duality"
    - "on the separability of parallel miso broadcast channels under partial csit a degrees of freedom region perspective"
    - "optimality of simple layered superposition coding in the 3 user miso bc with finite precision csit"
    - "on the optimality of treating interference as noise for interfering multiple access channels"
    - "on the optimality of treating inter cell interference as noise in uplink cellular networks"
    - "opportunistic treating interference as noise"
    - "generalized degrees of freedom of the symmetric k user interference channel under finite precision csit"
    - "optimality of treating interference as noise a combinatorial perspective"

Connected through hyperedge HE-2 (induced by source paper "on the separability of parallel miso broadcast channels under partial csit a degrees of freedom region perspective"):
  with members:
    - "degrees of freedom region of the m n 1 n 2 mimo broadcast channel with partial csit an application of sum set inequalities based on aligned image sets"
    - "dof region of the miso bc with partial csit proof by inductive fourier motzkin elimination"
    - "optimality of simple layered superposition coding in the 3 user miso bc with finite precision csit"
    - "on the optimality of treating inter cell interference as noise in uplink cellular networks"
    - "on the dof of parallel miso bcs with partial csit total order and separability"
    - "optimal dof region of the k user miso bc with partial csit"
    - "elevated multiplexing and signal space partitioning in the 2 user mimo ic with partial csit"
    - "sum rate maximization for linearly precoded downlink multiuser miso systems with partial csit a rate splitting approach"

Connected through hyperedge HE-3 (induced by source paper "on the optimality of treating inter cell interference as noise downlink cellular networks and uplink downlink duality"):
  with members:
    - "on the optimality of treating interference as noise for interfering multiple access channels"
    - "on the optimality of treating inter cell interference as noise in uplink cellular networks"
    - "opportunistic treating interference as noise"
    - "optimality of treating interference as noise a combinatorial perspective"
    - "on the optimality of treating interference as noise compound interference networks"
    - "on the optimality of treating interference as noise general message sets"
    - "on the optimality of treating interference as noise for k user parallel gaussian interference networks"
    - "wireless network information flow a deterministic approach"

Connected through hyperedge HE-4 (induced by source paper "gdof of interference channel with limited cooperation under finite precision csit"):
  with members:
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "channels with cooperation links that may be absent"
    - "sum set inequalities from aligned image sets instruments for robust gdof bounds"
    - "generalized degrees of freedom of the symmetric k user interference channel under finite precision csit"
    - "on the synergistic benefits of alternating csit for the miso bc"
    - "interference channels with source cooperation"
    - "interference channels with destination cooperation"
    - "interference mitigation through limited transmitter cooperation"

Connected through hyperedge HE-5 (induced by source paper "optimality of simple layered superposition coding in the 3 user miso bc with finite precision csit"):
  with members:
    - "k user symmetric m times n mimo interference channel under finite precision csit a gdof perspective"
    - "sum set inequalities from aligned image sets instruments for robust gdof bounds"
    - "aligned image sets and the generalized degrees of freedom of symmetric mimo interference channel with partial csit"
    - "network coherence time matters aligned image sets and the degrees of freedom of interference networks with finite precision csit and perfect csir"
    - "achievable dof regions of mimo networks with imperfect csit"
    - "gdof of the k user symmetric miso bc bridging the gap between finite precision and perfect csit"
    - "generalized degrees of freedom of the symmetric k user interference channel under finite precision csit"
    - "tdma is optimal for all unicast dof region of tim if and only if topology is chordal bipartite"

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)