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: "inferring social structure and dominance relationships between rhesus macaques using rfid tracking data"
  Abstract: In this paper we address the problem of inferring social structure and dominance relationships in a group of rhesus macaques (a species of monkey) using only position data captured using RFID tags. Automatic inference of the social structure in an animal group enables a number of important capabilities, including: 1) A verifiable measure of how the social structure is affected by an intervention such as a change in the environment, or the introduction of another animal, and 2) A potentially significant reduction in person hours normally used for assessing these changes. Social structure in a group is an important indicator of its members’ relative level of access to resources and has interesting implications for an individual’s health [Tunga et al. 2012] and learning in groups [Drea and Wallen 1999]. There are two main quantitative criteria assessed in order to infer the social structure; Time spent close to conspecifics, and displacements. An interaction matrix is used to represent the total duration of events detected as grooming behavior between any two monkeys. This forms an undirected tiestrength (closeness of relationships) graph. A directed graph of hierarchy is constructed by using the well cited assumption of a linear hierarchy for rhesus macaques [Brent et al. 2013][Maestripieri and Hoffman 2012]. Events that contribute to the adjacency matrix for this graph are withdrawals or displacements where a lower ranked monkey moves away from a higher ranked monkey. Displacements are one of the observable behaviors that can act as a strong indication of tie-strength and dominance. To quantify the directedness of interaction during these events we construct histograms of the dot products of motion orientation and relative position. This gives us a measure of how much time a monkey spends in moving towards or away from other group members. We present results of an analysis based on these approaches for a group of 6 male monkeys that were tracked over a period of 60 days at the Yerkes National Primate Research Center 1 .

Connected through hyperedge HE-1 (induced by source paper "unique identification of macaques for population monitoring and control"):
  with members:
    - "face recognition primates in the wild"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "neural network based clustering using pairwise constraints"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "densenet implementing efficient convnet descriptor pyramids"

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.SI(Social and Information Networks)