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: "characterizing degrees of freedom through additive combinatorics"
  Abstract: We establish a formal connection between the problem of characterizing degrees of freedom (DoF) in constant single-antenna interference channels (ICs), with general channel matrix, and the field of additive combinatorics. The theory we develop is based on a recent breakthrough result by Hochman in fractal geometry. Our first main contribution is an explicit condition on the channel matrix to admit full, i.e., $K/2$ DoF; this condition is satisfied for almost all channel matrices. We also provide a construction of corresponding DoF-optimal input distributions. The second main result is a new DoF-formula exclusively in terms of Shannon entropies. This formula is more amenable to both analytical statements and numerical evaluations than the DoF-formula by Wu et al., which is in terms of R\'enyi information dimension. We then use the new DoF-formula to shed light on the hardness of finding the exact number of DoF in ICs with rational channel coefficients, and to improve the best known bounds on the DoF of a well-studied channel matrix.

Connected through hyperedge HE-1 (induced by source paper "necessary conditions for k 2 degrees of freedom"):
  with members:
    - "degrees of freedom in vector interference channels"
    - "real interference alignment exploiting the potential of single antenna systems"
    - "sumset and inverse sumset inequalities for differential entropy and mutual information"

Connected through hyperedge HE-2 (induced by source paper "entropy bounds on abelian groups and the ruzsa divergence"):
  with members:
    - "forward and reverse entropy power inequalities in convex geometry"
    - "degrees of freedom in vector interference channels"
    - "entropies of weighted sums in cyclic groups and an application to polar codes"
    - "beyond the entropy power inequality via rearrangements"
    - "sumset and inverse sumset inequalities for differential entropy and mutual information"
    - "a new entropy power inequality for integer valued random variables"
    - "entropy and set cardinality inequalities for partition determined functions"
    - "monotonicity thinning and discrete versions of the entropy power inequality"

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)