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: "how to securely compute the modulo two sum of binary sources"
  Abstract: In secure multiparty computation, mutually distrusting users in a network want to collaborate to compute functions of data which is distributed among the users. The users should not learn any additional information about the data of others than what they may infer from their own data and the functions they are computing. Previous works have mostly considered the worst case context (i.e., without assuming any distribution for the data); Lee and Abbe (2014) is a notable exception. Here, we study the average case (i.e., we work with a distribution on the data) where correctness and privacy is only desired asymptotically. #R##N#For concreteness and simplicity, we consider a secure version of the function computation problem of K\"orner and Marton (1979) where two users observe a doubly symmetric binary source with parameter p and the third user wants to compute the XOR. We show that the amount of communication and randomness resources required depends on the level of correctness desired. When zero-error and perfect privacy are required, the results of Data et al. (2014) show that it can be achieved if and only if a total rate of 1 bit is communicated between every pair of users and private randomness at the rate of 1 is used up. In contrast, we show here that, if we only want the probability of error to vanish asymptotically in block length, it can be achieved by a lower rate (binary entropy of p) for all the links and for private randomness; this also guarantees perfect privacy. We also show that no smaller rates are possible even if privacy is only required asymptotically.

Connected through hyperedge HE-1 (induced by source paper "secure modulo sum via multiple access channel"):
  with members:
    - "secure computation and forward with linear codes"
    - "secure physical layer network coding versus secure network coding"
    - "physical layer network coding a random coding error exponent perspective"
    - "secure multiplex coding with dependent and non uniform multiple messages"
    - "on secure computation over the binary modulo 2 adder multiple access wiretap channel"
    - "expanding the compute and forward framework unequal powers signal levels and multiple linear combinations"
    - "secure compute and forward in a bidirectional relay"
    - "adaptive compute and forward with lattice codes over algebraic integers"

Connected through hyperedge HE-2 (induced by source paper "on secure computation over the binary modulo 2 adder multiple access wiretap channel"):
  with members:
    - "nomographic functions efficient computation in clustered gaussian sensor networks"
    - "two shannon type problems on secure multi party computations"
    - "distributed function computation with confidentiality"
    - "computation over mismatched channels"
    - "robust analog function computation via wireless multiple access channels"
    - "when is a function securely computable"
    - "the gaussian multiple access wire tap channel"

Connected through hyperedge HE-3 (induced by source paper "communication and randomness lower bounds for secure computation"):
  with members:
    - "assisted common information with an application to secure two party sampling"
    - "two shannon type problems on secure multi party computations"
    - "lower bounds on information complexity via zero communication protocols and applications"
    - "the infinite message limit of two terminal interactive source coding"

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)