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: "fairest constant sum rate transmission for cooperative data exchange an m convex minimization approach"
  Abstract: We consider the fairness in cooperative data exchange (CDE) problem among a set of wireless clients. In this system, each client initially obtains a subset of the packets. They exchange packets in order to reconstruct the entire packet set. We study the problem of how to find a transmission strategy that distributes the communication load most evenly in all strategies that have the same sum-rate (the total number of transmissions) and achieve universal recovery (the situation when all clients recover the packet set). We formulate this problem by a discrete minimization problem and prove its $M$-convexity. We show that our results can also be proved by the submodularity of the feasible region shown in previous works and are closely related to the resource allocation problems under submodular constraints. To solve this problem, we propose to use a steepest descent algorithm (SDA) based on $M$-convexity. By varying the number of clients and packets, we compare SDA with a deterministic algorithm (DA) based on submodularity in terms of convergence performance and complexity. The results show that for the problem of finding the fairest and minimum sum-rate strategy for the CDE problem SDA is more efficient than DA when the number of clients is up to five.

Connected through hyperedge HE-1 (induced by source paper "attaining fairness in communication for omniscience"):
  with members:
    - "a faster algorithm for asymptotic communication for omniscience"
    - "structured convex optimization under submodular constraints"
    - "coded cooperative data exchange in multihop networks"
    - "on coding for cooperative data exchange"
    - "on tightness of mutual dependence upperbound for secret key capacity of multiple terminals"
    - "a quantitative measure of fairness and discrimination for resource allocation in shared computer systems"

Connected through hyperedge HE-2 (induced by source paper "communication for omniscience"):
  with members:
    - "fairness in communication for omniscience"
    - "a faster algorithm for asymptotic communication for omniscience"
    - "info clustering a mathematical theory for data clustering"
    - "estimating minimum sum rate for cooperative data exchange"
    - "iterative merging algorithm for cooperative data exchange"
    - "structured convex optimization under submodular constraints"
    - "coded cooperative data exchange in multihop networks"
    - "perfect omniscience perfect secrecy and steiner tree packing"

Connected through hyperedge HE-3 (induced by source paper "fairness in communication for omniscience"):
  with members:
    - "a game theoretic perspective on communication for omniscience"
    - "estimating minimum sum rate for cooperative data exchange"
    - "efficient algorithms for the data exchange problem"
    - "structured convex optimization under submodular constraints"
    - "coded cooperative data exchange in multihop networks"
    - "on coding for cooperative data exchange"
    - "a quantitative measure of fairness and discrimination for resource allocation in shared computer systems"

Connected through hyperedge HE-4 (induced by source paper "a faster algorithm for asymptotic communication for omniscience"):
  with members:
    - "info clustering a mathematical theory for data clustering"
    - "a game theoretic perspective on communication for omniscience"
    - "estimating minimum sum rate for cooperative data exchange"
    - "iterative merging algorithm for cooperative data exchange"
    - "efficient algorithms for the data exchange problem"
    - "coded cooperative data exchange in multihop networks"
    - "on coding for cooperative data exchange"

Connected through hyperedge HE-5 (induced by source paper "a game theoretic perspective on communication for omniscience"):
  with members:
    - "estimating minimum sum rate for cooperative data exchange"
    - "efficient algorithms for the data exchange problem"
    - "coded cooperative data exchange in multihop networks"
    - "on coding for cooperative data exchange"
    - "an axiomatic theory of fairness in network resource allocation"
    - "a quantitative measure of fairness and discrimination for resource allocation in shared computer 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)