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: "network inference from co occurrences"
  Abstract: The discovery of networks is a fundamental problem arising in numerous fields of science and technology, including communication systems, biology, sociology, and neuroscience. Unfortunately, it is often difficult, or impossible, to obtain data that directly reveal network structure, and so one must infer a network from incomplete data. This paper considers inferring network structure from "co-occurrence" data: observations that identify which network components (e.g., switches, routers, genes) carry each transmission but do not indicate the order in which they handle the transmission. Without order information, the number of networks that are consistent with the data grows exponentially with the size of the network (i.e., the number of nodes). Yet, the basic engineering/evolutionary principles underlying most networks strongly suggest that not all data-consistent networks are equally likely. In particular, nodes that co-occur in many observations are probably closely connected. With this in mind, we model the co-occurrence observations as independent realizations of a random walk on the network, subjected to a random permutation to account for the lack of order information. Treating permutations as missing data, we derive an expectation-maximization (EM) algorithm for estimating the random walk parameters. The model and EM algorithm significantly simplify the problem, but the computational complexity of the reconstruction process does grow exponentially in the length of each transmission path. For networks with long paths, the exact e-step may be computationally intractable. We propose a polynomial-time Monte Carlo EM algorithm based on importance sampling and derive conditions that ensure convergence of the algorithm with high probability. Simulations and experiments with Internet measurements demonstrate the promise of this approach.

Connected through hyperedge HE-1 (induced by source paper "secure friend discovery via privacy preserving and decentralized community detection"):
  with members:
    - "overlapping community detection in networks the state of the art and comparative study"
    - "demon a local first discovery method for overlapping communities"

Connected through hyperedge HE-2 (induced by source paper "dynamic network delay cartography"):
  with members:
    - "learning with submodular functions a convex optimization perspective"
    - "compressive sensing over graphs"

Connected through hyperedge HE-3 (induced by source paper "reconstructing a graph from path traces"):
  with members:
    - "inferring networks of diffusion and influence"

Connected through hyperedge HE-4 (induced by source paper "discovery of a missing disease spreader"):
  with members:
    - "discovering network behind infectious disease outbreak"
    - "node discovery problem for a social network"

Connected through hyperedge HE-5 (induced by source paper "discovering network behind infectious disease outbreak"):
  with members:
    - "analysing covert social network foundation behind terrorism disaster"
    - "node discovery problem for a social network"

Connected through hyperedge HE-6 (induced by source paper "node discovery in a networked organization"):
  with members:
    - "learning the structure of dynamic probabilistic networks"
    - "reflective visualisation and verbalisation of unconscious preference"
    - "analysing covert social network foundation behind terrorism disaster"
    - "search in power law networks"

Connected through hyperedge HE-7 (induced by source paper "node discovery problem for a social network"):
  with members:
    - "learning the structure of dynamic probabilistic networks"
    - "analysing covert social network foundation behind terrorism disaster"
    - "search in power law 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)