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: "on distributed computation in noisy random planar networks"
  Abstract: We consider distributed computation of functions of distributed data in random planar networks with noisy wireless links. We present a new algorithm for computation of the maximum value which is order optimal in the number of transmissions and computation time. We also adapt the histogram computation algorithm of Ying et al [1] to make the histogram computation time optimal.

Connected through hyperedge HE-1 (induced by source paper "broadcasting in noisy radio networks"):
  with members:
    - "randomized broadcast in radio networks with collision detection"
    - "broadcast throughput in radio networks routing vs network coding"
    - "a new perspective on vertex connectivity"
    - "fast structuring of radio networks large for multi message communications"
    - "bounds on contention management in radio networks"
    - "analyzing network coding gossip made easy"

Connected through hyperedge HE-2 (induced by source paper "graph codes for distributed instant message collection in an arbitrary noisy broadcast network"):
  with members:
    - "sum networks from undirected graphs construction and capacity analysis"
    - "energy efficient distributed coding for data collection in a noisy sparse network"
    - "computing linear transformations with unreliable components"
    - "update efficiency and local repairability limits for capacity approaching codes"
    - "computation over gaussian networks with orthogonal components"
    - "computing linear functions by linear coding over networks"
    - "network coding for computing cut set bounds"
    - "towards a queueing based framework for in network function computation"

Connected through hyperedge HE-3 (induced by source paper "on the solvability of 3s nt sum network a region decomposition and weak decentralized code method"):
  with members:
    - "on network coding for sum networks"
    - "network coding for computing cut set bounds"
    - "communicating the sum of sources over a network"
    - "feasible alphabets for communicating the sum of sources over a network"
    - "some bounds on the capacity of communicating the sum of sources"
    - "some results on communicating the sum of sources over a network"

Connected through hyperedge HE-4 (induced by source paper "a sampling technique of proving lower bounds for noisy computations"):
  with members:
    - "how hard is computing parity with noisy communications"

Connected through hyperedge HE-5 (induced by source paper "network coding for 3 s n t sum networks"):
  with members:
    - "on network coding for sum networks"
    - "communicating the sum of sources over a network"
    - "some results on communicating the sum of sources over a network"

Connected through hyperedge HE-6 (induced by source paper "on network coding for sum networks"):
  with members:
    - "communicating the sum of sources over a network"
    - "on the solvability of 3 source 3 terminal sum networks"

Connected through hyperedge HE-7 (induced by source paper "on the solvability of 3 source 3 terminal sum networks"):
  with members:
    - "communicating the sum of sources over a network"
    - "feasible alphabets for communicating the sum of sources over a network"
    - "a simple necessary and sufficient condition for the double unicast problem"
    - "some bounds on the capacity of communicating the sum of sources"
    - "some results on communicating the sum of sources over a network"

Connected through hyperedge HE-8 (induced by source paper "network flows for functions"):
  with members:
    - "on distributed function computation in structure free random networks"

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)