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: "modeling and inferring human intents and latent functional objects for trajectory prediction"
  Abstract: This paper is about detecting functional objects and inferring human intentions in surveillance videos of public spaces. People in the videos are expected to intentionally take shortest paths toward functional objects subject to obstacles, where people can satisfy certain needs (e.g., a vending machine can quench thirst), by following one of three possible intent behaviors: reach a single functional object and stop, or sequentially visit several functional objects, or initially start moving toward one goal but then change the intent to move toward another. Since detecting functional objects in low-resolution surveillance videos is typically unreliable, we call them "dark matter" characterized by the functionality to attract people. We formulate the Agent-based Lagrangian Mechanics wherein human trajectories are probabilistically modeled as motions of agents in many layers of "dark-energy" fields, where each agent can select a particular force field to affect its motions, and thus define the minimum-energy Dijkstra path toward the corresponding source "dark matter". For evaluation, we compiled and annotated a new dataset. The results demonstrate our effectiveness in predicting human intent behaviors and trajectories, and localizing functional objects, as well as discovering distinct functional classes of objects by clustering human motion behavior in the vicinity of functional objects.

Connected through hyperedge HE-1 (induced by source paper "time perception machine temporal point processes for the when where and what of activity prediction"):
  with members:
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "phased lstm accelerating recurrent network training for long or event based sequences"
    - "first person activity forecasting with online inverse reinforcement learning"
    - "recognizing fine grained and composite activities using hand centric features and script data"
    - "action temporal localization in untrimmed videos via multi stage cnns"
    - "hierarchical multiscale recurrent neural networks"
    - "a hierarchical recurrent encoder decoder for generative context aware query suggestion"

Connected through hyperedge HE-2 (induced by source paper "predicting human activities using stochastic grammar"):
  with members:
    - "structural rnn deep learning on spatio temporal graphs"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"
    - "learning human activities and object affordances from rgb d videos"

Connected through hyperedge HE-3 (induced by source paper "forecasting interactive dynamics of pedestrians with fictitious play"):
  with members:
    - "intention from motion"
    - "recurrent neural networks for driver activity anticipation via sensory fusion architecture"
    - "car that knows before you do anticipating maneuvers via learning temporal driving models"
    - "dense optical flow prediction from a static image"
    - "anticipating the future by watching unlabeled video"
    - "pedestrian detection aided by deep learning semantic tasks"

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.CV(Computer Vision and Pattern Recognition)