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: "structured interference mitigation in two hop networks"
  Abstract: We consider two-hop S-R-D Gaussian networks with a source (S), a relay (R) and a destination (D), some of which experience additive interference. This additive interference, which renders the channels state-dependent, is either a) experienced at the destination D and known non-causally at the source S, or b) experienced at the relay R and known at the destination D. In both cases, one would hope to exploit this knowledge of the channel state at some of the nodes to obtain "clean" or interference-free channels, just as Costa's dirty-paper coding does for one-hop channels with state non-causally known to the transmitter. We demonstrate a scheme which achieves to within 0.5 bit of a "clean" channel. This novel scheme is based on nested-lattice code and a Decode-and-Forward (DF) relay. Intuitively, this strategy uses the structure provided by nested lattice codes to cancel the "integer" (or lattice quantized) part of the interference and treats the "residual" (or quantization noise) as noise.

Connected through hyperedge HE-1 (induced by source paper "relay channel with orthogonal components and structured interference known at the source"):
  with members:
    - "bounds on the capacity of the relay channel with noncausal state at source"
    - "capacity bounds and exact results for the cognitive z interference channel"
    - "bounds on the capacity of the relay channel with noncausal state information at source"
    - "lecture notes on network information theory"
    - "dirty paper coding for fading channels with partial transmitter side information"
    - "writing on dirty paper with resizing and its application to quasi static fading broadcast channels"
    - "on the fading paper achievable region of the fading mimo broadcast channel"

Connected through hyperedge HE-2 (induced by source paper "lattice codes for the gaussian relay channel decode and forward and compress and forward"):
  with members:
    - "the approximate sum capacity of the symmetric gaussian k user interference channel"
    - "wireless network information flow a deterministic approach"
    - "reliable physical layer network coding"
    - "nested lattice codes for gaussian relay networks with interference"
    - "computation alignment capacity approximation without noise accumulation"
    - "a lattice compress and forward scheme"
    - "interference alignment at finite snr for time invariant channels"
    - "list decoding for nested lattices and applications to relay channels"

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)