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: "multiuser detection in a dynamic environment part i user identification and data detection"
  Abstract: In random-access communication systems, the number of active users varies with time, and has considerable bearing on receiver's performance. Thus, techniques aimed at identifying not only the information transmitted, but also that number, play a central role in those systems. An example of application of these techniques can be found in multiuser detection (MUD). In typical MUD analyses, receivers are based on the assumption that the number of active users is constant and known at the receiver, and coincides with the maximum number of users entitled to access the system. This assumption is often overly pessimistic, since many users might be inactive at any given time, and detection under the assumption of a number of users larger than the real one may impair performance. The main goal of this paper is to introduce a general approach to the problem of identifying active users and estimating their parameters and data in a random-access system where users are continuously entering and leaving the system. The tool whose use we advocate is random-set theory (RST): applying this, we derive optimum receivers in an environment where the set of transmitters comprises an unknown number of elements. In addition, we can derive Bayesian-filter equations which describe the evolution with time of the a posteriori probability (APP) density of the unknown user parameters, and use this density to derive optimum detectors. In this paper, we restrict ourselves to interferer identification and data detection, while in a companion paper we shall examine the more complex problem of estimating users' parameters.

Connected through hyperedge HE-1 (induced by source paper "random access an information theoretic perspective"):
  with members:
    - "wireless network information flow a deterministic approach"
    - "on off random access channels a compressed sensing framework"
    - "a deterministic approach to wireless relay networks"

Connected through hyperedge HE-2 (induced by source paper "reduced dimension multiuser detection"):
  with members:
    - "asynchronous code division random access using convex optimization"
    - "structured compressed sensing from theory to applications"
    - "large system analysis of multiuser detection with an unknown number of users a high snr approach"
    - "noise folding in compressed sensing"
    - "xampling signal acquisition and processing in union of subspaces"
    - "time delay estimation from low rate samples a union of subspaces approach"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "support recovery of sparse signals"

Connected through hyperedge HE-3 (induced by source paper "large system analysis of multiuser detection with an unknown number of users a high snr approach"):
  with members:
    - "estimation in gaussian noise properties of the minimum mean square error"
    - "tight bounds on the capacity of binary input random cdma systems"
    - "analysis of belief propagation for non linear problems the example of cdma or how to prove tanaka s formula"
    - "randomly spread cdma asymptotics via statistical physics"

Connected through hyperedge HE-4 (induced by source paper "new bounds for binary and ternary overloaded cdma"):
  with members:
    - "errorless codes for over loaded cdma with active user detection"
    - "a class of errorless codes for over loaded synchronous wireless and optical cdma systems"

Connected through hyperedge HE-5 (induced by source paper "errorless codes for over loaded cdma with active user detection"):
  with members:
    - "a class of errorless codes for over loaded synchronous wireless and optical cdma systems"

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)