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: "iterative merging algorithm for cooperative data exchange"
  Abstract: We consider the problem of finding the minimum sum-rate strategy in cooperative data exchange systems that do not allow packet-splitting (NPS-CDE). In an NPS-CDE system, there are a number of geographically close cooperative clients who send packets to help the others recover a packet set. A minimum sum-rate strategy is the strategy that achieves universal recovery (the situation when all the clients recover the whole packet set) with the the minimal sum-rate (the total number of transmissions). We propose an iterative merging (IM) algorithm that recursively merges client sets based on a lower estimate of the minimum sum-rate and updates to the value of the minimum sum-rate. We also show that a minimum sum-rate strategy can be learned by allocating rates for the local recovery in each merged client set in the IM algorithm. We run an experiment to show that the complexity of the IM algorithm is lower than that of the existing deterministic algorithm when the number of clients is lower than $94$.

Connected through hyperedge HE-1 (induced by source paper "part ii a practical approach for successive omniscience"):
  with members:
    - "part i improving computational efficiency of communication for omniscience"
    - "attaining fairness in communication for omniscience"
    - "improving computational efficiency of communication for omniscience and successive omniscience"
    - "successive local and successive global omniscience"
    - "a practical approach for successive omniscience"
    - "universal multiparty data exchange and secret key agreement"
    - "a faster algorithm for asymptotic communication for omniscience"
    - "efficient algorithms for the data exchange problem"

Connected through hyperedge HE-2 (induced by source paper "improving computational efficiency of communication for omniscience and successive omniscience"):
  with members:
    - "a practical approach for successive omniscience"
    - "a faster algorithm for asymptotic communication for omniscience"
    - "efficient algorithms for the data exchange problem"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "a practical approach for successive omniscience"):
  with members:
    - "communication for omniscience"
    - "efficient algorithms for the data exchange problem"
    - "coded cooperative data exchange in multihop networks"
    - "on coding for cooperative data exchange"

Connected through hyperedge HE-4 (induced by source paper "info clustering a mathematical theory for data clustering"):
  with members:
    - "bounds on the communication rate needed to achieve sk capacity in the hypergraphical source model"
    - "duality between feature selection and data clustering"
    - "fairness in communication for omniscience"
    - "incremental and decremental secret key agreement"
    - "on the public communication needed to achieve sk capacity in the multiterminal source model"
    - "deep residual learning for image recognition"
    - "the communication complexity of achieving sk capacity in a class of pin models"
    - "parallel correlation clustering on big graphs"

Connected through hyperedge HE-5 (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"
    - "fairest constant sum rate transmission for cooperative data exchange an m convex minimization approach"
    - "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-6 (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"
    - "fairest constant sum rate transmission for cooperative data exchange an m convex minimization approach"
    - "efficient algorithms for the data exchange problem"
    - "coded cooperative data exchange in multihop networks"
    - "on coding for cooperative data exchange"

Connected through hyperedge HE-7 (induced by source paper "incremental and decremental secret key agreement"):
  with members:
    - "on the public communication needed to achieve sk capacity in the multiterminal source model"
    - "the communication complexity of achieving sk capacity in a class of pin models"
    - "achieving sk capacity in the source model when must all terminals talk"
    - "coded cooperative data exchange for a secret key"
    - "perfect omniscience perfect secrecy and steiner tree packing"
    - "secret key generation for a pairwise independent network model"
    - "on tightness of mutual dependence upperbound for secret key capacity of multiple terminals"

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.DS(Data Structures and Algorithms)