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: "cooperative compute and forward"
  Abstract: We examine the benefits of user cooperation under compute-and-forward. Much like in network coding, receivers in a compute-and-forward network recover finite-field linear combinations of transmitters' messages. Recovery is enabled by linear codes: transmitters map messages to a linear codebook, and receivers attempt to decode the incoming superposition of signals to an integer combination of codewords. However, the achievable computation rates are low if channel gains do not correspond to a suitable linear combination. In response to this challenge, we propose a cooperative approach to compute-and-forward. We devise a lattice-coding approach to block Markov encoding with which we construct a decode-and-forward style computation strategy. Transmitters broadcast lattice codewords, decode each other's messages, and then cooperatively transmit resolution information to aid receivers in decoding the integer combinations. Using our strategy, we show that cooperation offers a significant improvement both in the achievable computation rate and in the diversity-multiplexing tradeoff.

Connected through hyperedge HE-1 (induced by source paper "study of buffer aided physical layer network coding with ml and mmse linear code designs for cooperative ds cdma systems"):
  with members:
    - "iterative detection and decoding algorithms for mimo systems in block fading channels using ldpc codes"
    - "robust adaptive beamforming based on low rank and cross correlation techniques"
    - "adaptive reduced rank receive processing based on minimum symbol error rate criterion for large scale multiple antenna systems"
    - "building blocks of physical layer network coding"
    - "adaptive distributed space time coding based on adjustable code matrices for cooperative mimo relaying systems"

Connected through hyperedge HE-2 (induced by source paper "nomographic functions efficient computation in clustered gaussian sensor networks"):
  with members:
    - "interactive computation of type threshold functions in collocated broadcast superposition networks"
    - "computation over mismatched channels"
    - "robust analog function computation via wireless multiple access channels"
    - "distributed detection over gaussian multiple access channels with constant modulus signaling"
    - "compute and forward harnessing interference through structured codes"
    - "on the role of mmse estimation in approaching the information theoretic limits of linear gaussian channels shannon meets wiener"

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)