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: "interference alignment using finite and dependent channel extensions the single beam case"
  Abstract: Vector space interference alignment (IA) is known to achieve high degrees of freedom (DoF) with infinite independent channel extensions, but its performance is largely unknown for a finite number of possibly dependent channel extensions. In this paper, we consider a $K$-user $M_t \times M_r$ MIMO interference channel (IC) with arbitrary number of channel extensions $T$ and arbitrary channel diversity order $L$ (i.e., each channel matrix is a generic linear combination of $L$ fixed basis matrices). We study the maximum DoF achievable via vector space IA in the single beam case (i.e. each user sends one data stream). We prove that the total number of users $K$ that can communicate interference-free using linear transceivers is upper bounded by $NL+N^2/4$, where $N = \min\{M_tT, M_rT \}$. An immediate consequence of this upper bound is that for a SISO IC the DoF in the single beam case is no more than $\min\left\{\sqrt{ 5K/4}, L + T/4\right\}$. When the channel extensions are independent, i.e. $ L$ achieves the maximum $M_r M_t T $, we show that this maximum DoF lies in $[M_r+M_t-1, M_r+M_t]$ regardless of $T$. Unlike the well-studied constant MIMO IC case, the main difficulty is how to deal with a hybrid system of equations (zero-forcing condition) and inequalities (full rank condition). Our approach combines algebraic tools that deal with equations with an induction analysis that indirectly considers the inequalities.

Connected through hyperedge HE-1 (induced by source paper "revisiting landscape analysis in deep neural networks eliminating decreasing paths to infinity"):
  with members:
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "on exact computation with an infinitely wide neural net"
    - "elimination of all bad local minima in deep learning"
    - "on the convergence of adaptive gradient methods for nonconvex optimization"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "on the connection between learning two layers neural networks and tensor decomposition"
    - "learning one hidden layer neural networks under general input distributions"
    - "over parameterized deep neural networks have no strict local minima for any continuous activations"

Connected through hyperedge HE-2 (induced by source paper "modulation and multiple access for 5g networks"):
  with members:
    - "power domain non orthogonal multiple access noma in 5g systems potentials and challenges"
    - "non orthogonal multiple access in large scale heterogeneous networks"
    - "capacity comparison between mimo noma and mimo oma with multiple users in a cluster"
    - "wireless powered communications with non orthogonal multiple access"
    - "nonorthogonal multiple access in large scale underlay cognitive radio networks"
    - "design of massive mimo noma with limited feedback"
    - "optimal joint power and subcarrier allocation for mc noma systems"
    - "relay selection for cooperative noma"

Connected through hyperedge HE-3 (induced by source paper "upper bounds of interference alignment degree of freedom"):
  with members:
    - "settling the feasibility of interference alignment for the mimo interference channel the symmetric square case"
    - "on feasibility of interference alignment in mimo interference networks"
    - "on feasibility of interference alignment in mimo interference networks"

Connected through hyperedge HE-4 (induced by source paper "feasibility of interference alignment for the mimo interference channel"):
  with members:
    - "real interference alignment exploiting the potential of single antenna systems"
    - "on the number of interference alignment solutions for the k user mimo channel with constant coefficients"
    - "the approximate sum capacity of the symmetric gaussian k user interference channel"
    - "on the degrees of freedom achievable through interference alignment in a mimo interference channel"
    - "a feasibility test for linear interference alignment in mimo channels with constant coefficients"
    - "interactive interference alignment"
    - "degrees of freedom of interference channels with comp transmission and reception"
    - "subspace alignment chains and the degrees of freedom of the three user mimo interference 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)