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: "computation in multicast networks function alignment and converse theorems"
  Abstract: The classical problem in network coding theory considers communication over multicast networks. Multiple transmitters send independent messages to multiple receivers which decode the same set of messages. In this work, computation over multicast networks is considered: each receiver decodes an identical function of the original messages. For a countably infinite class of two-transmitter two-receiver single-hop linear deterministic networks, the computing capacity is characterized for a linear function (modulo-2 sum) of Bernoulli sources. Inspired by the geometric concept of interference alignment in networks, a new achievable coding scheme called function alignment is introduced. A new converse theorem is established that is tighter than cut-set based and genie-aided bounds. Computation (vs. communication) over multicast networks requires additional analysis to account for multiple receivers sharing a network's computational resources. We also develop a network decomposition theorem which identifies elementary parallel subnetworks that can constitute an original network without loss of optimality. The decomposition theorem provides a conceptually-simpler algebraic proof of achievability that generalizes to $L$-transmitter $L$-receiver networks.

Connected through hyperedge HE-1 (induced by source paper "two way function computation"):
  with members:
    - "two way interference channel capacity how to have the cake and eat it too"
    - "feedback through overhearing"
    - "capacity of a post channel with and without feedback"
    - "perfect output feedback in the two user decentralized interference channel"
    - "two way interference channels"
    - "on network coding for sum networks"
    - "two way networks when adaptation is useless"
    - "wireless network information flow a deterministic approach"

Connected through hyperedge HE-2 (induced by source paper "two way interference channel capacity how to have the cake and eat it too"):
  with members:
    - "two way function computation"
    - "fundamental limits of caching"
    - "two way interference channels"
    - "approximate feedback capacity of the gaussian multicast channel"
    - "two way networks when adaptation is useless"
    - "wireless network information flow a deterministic approach"
    - "interference channels with source cooperation"
    - "interference channels with rate limited feedback"

Connected through hyperedge HE-3 (induced by source paper "interactive function computation via polar coding"):
  with members:
    - "achieving marton s region for broadcast channels using polar codes"
    - "how to achieve the capacity of asymmetric channels"
    - "polar codes for q ary channels q 2 r"
    - "how to construct polar codes"
    - "channel polarization on q ary discrete memoryless channels by arbitrary kernels"
    - "polar codes for the m user mac"
    - "when is a function securely computable"
    - "polar codes are optimal for lossy source coding"

Connected through hyperedge HE-4 (induced by source paper "computation over gaussian networks with orthogonal components"):
  with members:
    - "wireless network information flow a deterministic approach"
    - "nested lattice codes for gaussian relay networks with interference"
    - "computing linear functions by linear coding over networks"
    - "network coding for computing cut set bounds"
    - "optimal function computation in directed and undirected graphs"
    - "linear codes target function classes and network computing capacity"
    - "cascade multiterminal source coding"
    - "compute and forward harnessing interference through structured codes"

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)