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: "degrees of freedom in vector interference channels"
  Abstract: This paper continues the Wu–Shamai–Verdu program on characterizing the degrees of freedom (DoF) of interference channels (ICs) through Renyi information dimension. Specifically, we find a single-letter formula for the DoF of vector ICs, encompassing multiple-input multiple-output ICs, time- and/or frequency-selective ICs, and combinations thereof, as well as scalar ICs as considered by Wu  et al. , 2015. The DoF-formula we obtain lower-bounds the DoF of  all  channels—with respect to the choice of the channel matrix—and upper-bounds the DoF of  almost all  channels. It applies to a large class of noise distributions, and its proof is based on an extension of a result by Guionnet and Shlyakthenko, 2007, to the vector case in combination with the Ruzsa triangle inequality for differential entropy introduced by Kontoyiannis and Madiman, 2015. As in scalar ICs, achieving full DoF requires the use of singular input distributions. Strikingly, in the vector case, it suffices to enforce singularity on the joint distribution of each transmit vector. This can be realized through signaling in subspaces of the ambient signal space, which is in accordance with the idea of interference alignment, and, most importantly, allows the scalar entries of the transmit vectors to have non-singular distributions. The DoF-formula for vector ICs we obtain enables a unified treatment of classical interference alignment a la Cadambe and Jafar, 2008, and Maddah-Ali  et al. , 2008, and the number-theoretic schemes proposed by Motahari  et al. , 2014, and by Etkin and Ordentlich, 2009. Moreover, it allows to calculate the DoF achieved by new signaling schemes for vector ICs. We furthermore recover the result by Cadambe and Jafar on the non-separability of parallel ICs, 2009, and we show that almost all parallel ICs are separable in terms of DoF. Finally, our results apply to complex vector ICs, thereby extending the main findings of Wu  et al. , 2015 to the complex case.

Connected through hyperedge HE-1 (induced by source paper "necessary conditions for k 2 degrees of freedom"):
  with members:
    - "characterizing degrees of freedom through additive combinatorics"
    - "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 "on the information dimension of stochastic processes"):
  with members:
    - "the shannon lower bound is asymptotically tight"
    - "compression based compressed sensing"
    - "universal compressed sensing"
    - "optimal phase transitions in compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "on the information dimension of multivariate gaussian processes"):
  with members:
    - "on the information dimension of stochastic processes"
    - "the shannon lower bound is asymptotically tight"

Connected through hyperedge HE-4 (induced by source paper "the shannon lower bound is asymptotically tight"):
  with members:
    - "data compression with low distortion and finite blocklength"
    - "rate distortion bounds for high resolution vector quantization via gibbs s inequality"

Connected through hyperedge HE-5 (induced by source paper "entropy and source coding for integer dimensional singular random variables"):
  with members:
    - "degrees of freedom of generic block fading mimo channels without a priori channel state information"

Connected through hyperedge HE-6 (induced by source paper "entropies of weighted sums in cyclic groups and an application to polar codes"):
  with members:
    - "entropy bounds on abelian groups and the ruzsa divergence"
    - "unified scaling of polar codes error exponent scaling exponent moderate deviations and error floors"
    - "finite length scaling for polar codes"
    - "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"
    - "polar codes for q ary channels q 2 r"
    - "monotonicity thinning and discrete versions of the entropy power inequality"

Connected through hyperedge HE-7 (induced by source paper "entropy bounds on abelian groups and the ruzsa divergence"):
  with members:
    - "forward and reverse entropy power inequalities in convex geometry"
    - "characterizing degrees of freedom through additive combinatorics"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "characterizing degrees of freedom through additive combinatorics"):
  with members:
    - "real interference alignment exploiting the potential of single antenna systems"
    - "explicit and almost sure conditions for k 2 degrees of freedom"

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)