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: "exploiting opportunistic multiuser detection in decentralized multiuser mimo systems"
  Abstract: This paper studies the design of a decentralized multiuser multi-antenna (MIMO) system for spectrum sharing over a fixed narrow band, where the coexisting users independently update their transmit covariance matrices for individual transmit-rate maximization via an iterative manner. This design problem was usually investigated in the literature by assuming that each user treats the co-channel interference from all the other users as additional (colored) noise at the receiver, i.e., the conventional single-user decoder (SUD) is applied. This paper proposes a new decoding method for the decentralized multiuser MIMO system, whereby each user opportunistically cancels the co-channel interference from some or all of the other users via applying multiuser detection techniques, thus termed opportunistic multiuser detection (OMD). This paper studies the optimal transmit covariance design for users' iterative maximization of individual transmit rates with the proposed OMD, and demonstrates the resulting capacity gains in decentralized multiuser MIMO systems against the conventional SUD.

Connected through hyperedge HE-1 (induced by source paper "wireless communications in the era of big data"):
  with members:
    - "mobile crowd sensing and computing when participatory sensing meets participatory social media"
    - "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks"
    - "min flow rate maximization for software defined radio access networks"

Connected through hyperedge HE-2 (induced by source paper "zero outage cellular downlink with fixed rate d2d underlay"):
  with members:
    - "a survey on device to device communication in cellular networks"
    - "low rate machine type communication via wireless device to device d2d links"
    - "five disruptive technology directions for 5g"
    - "random deployment of data collectors for serving randomly located sensors"
    - "iterative spectrum shaping with opportunistic multiuser detection"

Connected through hyperedge HE-3 (induced by source paper "opportunistic interference mitigation achieves optimal degrees of freedom in wireless multi cell uplink networks"):
  with members:
    - "real interference alignment exploiting the potential of single antenna systems"
    - "parallel opportunistic routing in wireless networks"
    - "interference alignment with limited feedback on two cell interfering two user mimo mac"
    - "from spectrum pooling to space pooling opportunistic interference alignment in mimo cognitive networks"
    - "opportunistic relaying in wireless networks"
    - "ergodic interference alignment"
    - "opportunistic spatial orthogonalization and its application in fading cognitive radio networks"
    - "opportunistic interference alignment in mimo interference channels"

Connected through hyperedge HE-4 (induced by source paper "simple transmission strategies for interference channel"):
  with members:
    - "the capacity region of the mimo interference channel and its reciprocity to within a constant gap"
    - "on the generalized degrees of freedom of the k user symmetric mimo gaussian interference channel"
    - "the approximate capacity of the many to one and one to many gaussian interference channels"
    - "lecture notes on network information theory"
    - "the two user gaussian interference channel a deterministic view"
    - "gaussian interference channel capacity to within one bit"

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)