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: "coalitional games in miso interference channels epsilon core and coalition structure stable set"
  Abstract: The multiple-input single-output interference channel is considered. Each transmitter is assumed to know the channels between itself and all receivers perfectly and the receivers are assumed to treat interference as additive noise. In this setting, noncooperative transmission does not take into account the interference generated at other receivers which generally leads to inefficient performance of the links. To improve this situation, we study cooperation between the links using coalitional games. The players ( links) in a coalition either perform zero forcing transmission or Wiener filter precoding to each other. The epsilon-core is a solution concept for coalitional games that takes into account the overhead required in coalition deviation. We provide necessary and sufficient conditions for the strong and weak epsilon-core of our coalitional game not to be empty with zero forcing transmission. Since, the epsilon-core only considers the possibility of joint cooperation of all links, we study coalitional games in partition form in which several distinct coalitions can form. We propose a polynomial-time distributed coalition formation algorithm based on coalition merging and prove that its solution lies in the coalition structure stable set of our coalition formation game. Simulation results reveal the cooperation gains for different coalition formation complexities and deviation overhead models.

Connected through hyperedge HE-1 (induced by source paper "distributed clustering for multiuser networks through coalition formation"):
  with members:
    - "distributed long term base station clustering in cellular networks using coalition formation"
    - "coalitional games in partition form for joint spectrum sensing and access in cognitive radio networks"

Connected through hyperedge HE-2 (induced by source paper "adaptive pilot clustering in heterogeneous massive mimo networks"):
  with members:
    - "pilot clustering in asymmetric massive mimo networks"
    - "massive mimo with non ideal arbitrary arrays hardware scaling laws and circuit aware design"
    - "blind pilot decontamination"
    - "massive mimo for maximal spectral efficiency how many users and pilots should be allocated"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "a coordinated approach to channel estimation in large scale multiple antenna systems"
    - "coalitional games in partition form for joint spectrum sensing and access in cognitive radio networks"
    - "achieving massive mimo spectral efficiency with a not so large number of antennas"

Connected through hyperedge HE-3 (induced by source paper "game theory for signal processing in networks"):
  with members:
    - "understanding game theory via wireless power control"
    - "crawford sobel meet lloyd max on the grid"
    - "multiobjective signal processing optimization the way to balance conflicting metrics in 5g systems"
    - "distributed cooperative sensing in cognitive radio networks an overlapping coalition formation approach"
    - "distributed adaptive networks a graphical evolutionary game theoretic view"
    - "jamming games in the mimo wiretap channel with an active eavesdropper"
    - "distributed learning policies for power allocation in multiple access channels"
    - "on the nash equilibria in decentralized parallel interference channels"

Connected through hyperedge HE-4 (induced by source paper "analyzing interference from static cellular cooperation using the nearest neighbour model"):
  with members:
    - "a stochastic geometry framework for analyzing pairwise cooperative cellular networks"
    - "backhaul constrained multi cell cooperation leveraging sparsity and spectral clustering"
    - "studying the sinr process of the typical user in poisson networks by using its factorial moment measures"
    - "a tractable model for noncoherent joint transmission base station cooperation"
    - "interference coordination random clustering and adaptive limited feedback"
    - "modeling and analysis of k tier downlink heterogeneous cellular networks"
    - "a tractable approach to coverage and rate in cellular networks"
    - "networked mimo with clustered linear precoding"

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)