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: "optimal computation of symmetric boolean functions in collocated networks"
  Abstract: We consider collocated wireless sensor networks, where each node has a Boolean measurement and the goal is to compute a given Boolean function of these measurements. We first consider the worst case setting and study optimal block computation strategies for computing symmetric Boolean functions. We study three classes of functions: threshold functions, delta functions and interval functions. We provide exactly optimal strategies for the first two classes, and a scaling law order-optimal strategy with optimal preconstant for interval functions. We also extend the results to the case of integer measurements and certain integer-valued functions. We use lower bounds from communication complexity theory, and provide an achievable scheme using information theoretic tools. #R##N#Next, we consider the case where nodes measurements are random and drawn from independent Bernoulli distributions. We address the problem of optimal function computation so as to minimize the expected total number of bits that are transmitted. In the case of computing a single instance of a Boolean threshold function, we show the surprising result that the optimal order of transmissions depends in an extremely simple way on the values of previously transmitted bits, and the ordering of the marginal probabilities of the Boolean variables. #R##N#The approach presented can be generalized to the case where each node has a block of measurements, though the resulting problem is somewhat harder, and we conjecture the optimal strategy. We further show how to generalize to a pulse model of communication. One can also consider the related problem of approximate computation given a fixed number of bits. In this case, the optimal strategy is significantly different, and lacks an elegant characterization. However, for the special case of the parity function, we show that the greedy strategy is optimal.

Connected through hyperedge HE-1 (induced by source paper "the stochastic score classification problem"):
  with members:
    - "submodular goal value of boolean functions"
    - "scenario submodular cover"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "communication cost for updating linear functions when message updates are sparse connections to maximally recoverable codes"):
  with members:
    - "weight enumerators and higher support weights of maximally recoverable codes"
    - "on partial maximally recoverable and maximally recoverable codes"
    - "file updates under random arbitrary insertions and deletions"
    - "synchronizing edits in distributed storage networks"
    - "optimal locally repairable and secure codes for distributed storage systems"
    - "codes with locality for two erasures"
    - "fundamental limits on communication for oblivious updates in storage networks"
    - "explicit maximally recoverable codes with locality"

Connected through hyperedge HE-3 (induced by source paper "condense a reconfigurable knowledge acquisition architecture for future 5g iot"):
  with members:
    - "network coding as a service"
    - "photonic delay systems as machine learning implementations"
    - "software defined networking a comprehensive survey"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "distributed function computation over a rooted directed tree"
    - "linear coding schemes for the distributed computation of subspaces"
    - "internet of things iot a vision architectural elements and future directions"
    - "on network coding for sum networks"

Connected through hyperedge HE-4 (induced by source paper "a relation between network computation and functional index coding problems"):
  with members:
    - "capacity of sum networks for different message alphabets"
    - "three stories on a two sided coin index coding locally recoverable distributed storage and guessing games on graphs"
    - "error correcting functional index codes generalized exclusive laws and graph coloring"
    - "index coding with coded side information"
    - "an equivalence between network coding and index coding"
    - "optimal index codes for a class of multicast networks with receiver side information"
    - "on network coding for sum networks"
    - "network coding for computing cut set bounds"

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)