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: "n k user interference channels degrees of freedom"
  Abstract: We analyze the gains of opportunistic communication in multiuser interference channels. Consider a fully connected $n$-user Gaussian interference channel. At each time instance only $K\leq n$ transmitters are allowed to be communicating with their respective receivers and the remaining $(n-K)$ transmitter-receiver pairs remain inactive. For finite $n$, if the transmitters can acquire channel state information (CSI) and if all channel gains are bounded away from zero and infinity, the seminal results on interference alignment establish that for any $K$ {\em arbitrary} active pairs the total number of spatial degrees of freedom per orthogonal time and frequency domain is $\frac{K}{2}$. Also it is noteworthy that without transmit-side CSI the interference channel becomes interference-limited and the degrees of freedom is 0. In {\em dense} networks ($n\rightarrow\infty$), however, as the size of the network increase, it becomes less likely to sustain the bounding conditions on the channel gains. By exploiting this fact, we show that when $n$ obeys certain scaling laws, by {\em opportunistically} and {\em dynamically} selecting the $K$ active pairs at each time instance, the number of degrees of freedom can exceed $\frac{K}{2}$ and in fact can be made arbitrarily close to $K$. More specifically when all transmitters and receivers are equipped with one antenna, then the network size scaling as $n\in\omega(\snr^{d(K-1)})$ is a {\em sufficient} condition for achieving $d\in[0,K]$ degrees of freedom. Moreover, achieving these degrees of freedom does not necessitate the transmitters to acquire channel state information. Hence, invoking opportunistic communication in the context of interference channels leads to achieving higher degrees of freedom that are not achievable otherwise.

Connected through hyperedge HE-1 (induced by source paper "on the degrees of freedom of the large scale interfering two way relay network"):
  with members:
    - "what will 5g be"
    - "an algebraic approach to physical layer network coding"
    - "degrees of freedom for mimo two way x relay channel"
    - "degrees of freedom of two hop wireless networks everyone gets the entire cake"
    - "opportunistic interference mitigation achieves optimal degrees of freedom in wireless multi cell uplink networks"
    - "reliable physical layer network coding"
    - "a simple cooperative diversity method based on network path selection"

Connected through hyperedge HE-2 (induced by source paper "on the scaling of interference alignment under delay and power constraints"):
  with members:
    - "channel diversity needed for vector space interference alignment"
    - "real interference alignment exploiting the potential of single antenna systems"
    - "on the degrees of freedom achievable through interference alignment in a mimo interference channel"
    - "the ergodic capacity of phase fading interference networks"
    - "ergodic interference alignment"
    - "interference alignment with asymmetric complex signaling settling the host madsen nosratinia conjecture"
    - "hierarchical cooperation achieves optimal capacity scaling in ad hoc networks"

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)